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Striking Teachers Won, but Are School-Funding Gains Sustainable and Equitable?
Without higher taxes, and redistribution of funding to poorer districts, #RedforEd victories in Arizona, North Carolina, Oklahoma, and West Virginia may be short-lived.
Dwyer Gunn
A #RedforEd teachers strike in Arizona.
(Photo: Ralph Freso/Getty Images)
In 2018, public school teachers won notable victories in some unexpected places. Teachers in West Virginia launched the #RedforEd movement, winning a 5 percent pay raise last February after a strike that stretched almost two weeks long. Public school teachers in Arizona, Oklahoma, North Carolina, and Kentucky followed suit in striking—winning various concessions from Republican-dominated legislatures and governors: a 9 percent state school-funding increase in Arizona; 19 percent in Oklahoma, and 3 percent in North Carolina, according to a report released last week by the Center on Budget and Policy Priorities. (In Kentucky, where teachers were protesting proposed changes to pensions, funding was essentially flat.)
The CBPP report, however, also notes that these victories may be short-lived. Not only is funding in all five #RedforEd states well below pre-Recession levels, but in increasing financial support, legislators in many of these states relied on funding sources that are both unsustainable and inequitable.
In Arizona, for example, the education bill—which increased funding for teacher salaries by $273 million and included an additional $100 million of flexible funding—did not actually include new revenue sources, with the exception of a small increase in the new car-registration fee, which will disproportionately harm low-income residents. It instead relies on Medicaid spending cuts and rosy economic growth projections. Similarly, legislators in North Carolina did not include new revenue sources in their education-funding bill and are facing a projected budget shortfall this year as deep income-tax cuts take effect. In Oklahoma, legislators relied on increasing cigarette and gas taxes, two types of taxes that can be unsustainable over time: People may smoke less, or purchase more fuel-efficient cars, in the face of them.
Michael Leachman, senior director of state fiscal research at CBPP and a co-author of the report, says that sustainable revenue sources are those that grow alongside school costs over time.
"You want to use revenues that will grow with school costs over time," Leachman says. "The best major funding source is income taxes. ... Income taxes are also typically graduated so they're based on ability to pay."
In recent years, some states have come around to this reality. In Kansas, for example, where districts and conservative legislators have been locked in a long-running court case over school funding, lawmakers in 2017 approved a rollback of the state's infamous income-tax cuts. (The vote came after the Kansas Supreme Court ruled that the state's schools were under-funded and ordered the legislature to come up with more money for public schools.) Minnesota, in 2013, meanwhile, raised taxes on high-income residents to fund full-day kindergarten and a variety of additional preschool and higher-education scholarships.
But while increasing income taxes is a sustainable way to increase the amount of state revenue available for school funding, it's also not the only piece of the puzzle. As Pacific Standard has covered before, most school districts receive funding from local, state, and federal sources. Public schools receive 45 percent of their funding from local sources, 46 percent from the state, and 9 percent from the feds. The problem with this funding mix is that it disadvantages districts with few sources of local wealth.
In theory, state and federal funding should make up for these disparities by directing more money to poorer districts. In practice, however, this rarely happens, and yawning funding disparities currently exist between rich and poor districts (and even between racially concentrated, low-income minority districts and comparable white districts). According to the most recent school funding national report card from the National Law Center, the majority of states have either flat or regressive funding systems, in which high-poverty districts receive either the same amount of state funding or less state funding than low-poverty districts. Only 11 states provide high-poverty districts with at least 5 percent more funding than low-poverty districts. (These stats are particularly egregious in light of the fact that education experts agree it costs more money, on a per-student basis, to educate low-income students.)
Simply raising income taxes won't be enough. Historically, school-funding advocates have argued for states to both increase overall spending on education—funded, perhaps, by higher income taxes on the wealthy—and to direct more of that money to high-poverty districts to make up for lower local revenues. But, Rebecca Sibilia, the founder and chief executive officer of EdBuild, a non-profit organization that researches school-funding issues, says that, while she'd like to see more states increase taxes on high earners, those revenues are vulnerable to what she calls "crowd-out."
"This is the conundrum that I think that everyone in the school-funding community is facing right now," Sibilia says. "Larger taxing jurisdictions are going to create more equity—in that respect, we would advocate for 100 percent of education funding to come from state funding. The problem is that all of the increasing burdens that these states are going to continue to face is going to cause crowd-out. We're seeing education money that states collect get sloughed off to cover increasing Medicaid costs, pension debt, that kind of thing."
Sibilia argues that, in light of these challenges, local property taxes can and should also play a role in achieving truly sustainable education-funding streams. Specifically, Sibilia suggests that states need to do two things: require districts to raise a certain amount of money locally—proportional to the district's wealth—in order to be eligible to receive state education funding, and impose a ceiling on the amount of locally raised revenue that can go to that district's schools.
"States need to say: If you're going to get help from the state, you have to raise money that's proportionate to the wealth that you're sitting on," Sibilia says. "And the ceiling is where you get your equity provision: if you raise more than X amount, some of it has to be reclaimed by the state."
Imposing both a floor and a ceiling would relieve some of the pressure on state funding, while also ensuring equity. At present, only 22 states have a "floor"—a minimum contribution districts must make in order to receive state funding—and only 19 states have a "ceiling." To establish sustainable and equitable school-funding streams, lawmakers will have to change that, but they'll also have to tackle another bogeyman: the anti-tax laws passed in recent decades.
"Almost every state over the last 20 years decided they were going to put the most ridiculous constraints on increasing taxes," she says. "One of the things we need to do is we need to start scaling back some of these ridiculous prohibitions that were put in place. We need to set the bar higher for what we should be investing in in education."
School FundingProperty TaxRed StateIncome TaxTeachers StrikePoverty
Dwyer Gunn is a Pacific Standard contributing writer. Her writing has appeared in the New York Times, Slate, Psychology Today, and elsewhere. She was previously the editor of the Freakonomics blog.
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Panel van takes Madiba's legacy to the people
Home Transport & Logistics Automotive Panel van takes Madiba's legacy to the people
Transport & LogisticsAutomotiveBusiness ManagementSocial Development
A Mercedes-Benz Sprinter 519 CDI Panel Van has been converted into a mobile museum for the Nelson Mandela Museum, based in Mthatha, Eastern Cape.
The vehicle has been ready to be used by the institution from 18 July, widely recognised the world over as Nelson Mandela Day, in celebration of the icon's birthday.
Mandela Day is a global call for individuals and responsible corporate citizens to follow in the footsteps of the iconic former president and political prisoner.
It was this same spirit that led to the establishment of the Nelson Mandela Museum in Mthatha, an institution that is part of a portfolio of legacy projects that seek to transform the heritage landscape, especially in Mandela's hometown.
Mercedes-Benz South Africa has long been associated with South Africa's first democratically elected president. It was a specially built red Mercedes-Benz S-Class that Mandela was driven in, after he was released from prison, in 1990.
"As Mercedes-Benz Vans South Africa, we are delighted to continue the legacy of Nelson Mandela, in his birthday month. What better way to do this than by assisting underprivileged communities of the Eastern Cape and surrounding areas to gain access to the exemplary icon that was Tata Madiba," said Nadia Trimmel, Vice-President of Mercedes-Benz Vans South Africa.
The 5.5-ton Extra-Long Sprinter is earmarked to assist the Nelson Mandela Museum in fulfilling a vision for Madiba's legacy to be a living and mobile museum that embraces development and inspires people through education, culture and tourism.
One of the barriers for the museum carrying out its mission has been accessibility. A large part of the region has not been exposed to the museum for various reasons, and this necessitated a mobile solution.
"The Sprinter will improve the reach of the museum's educational programmes and increase accessibility to various communities, especially those in far-flung areas of the Eastern Cape. The Mercedes-Benz Sprinter will be used to narrate the legacy of Mandela to those who need to be inspired most," said Bonke Tyhulu, CEO of the Nelson Mandela Museum.
The devastating impact of Covid-19 has also further highlighted the need for the museum to initiate mobile educational programmes, allowing the institution to promote the ideals of non-racialism, non-sexism, peace, unity, human rights and democracy.
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LABEL PROFILE Where to Start With the Essential Jazz and Soul of Mainstream Records By Marcus J. Moore · September 25, 2020
From 1964 to 1978, Mainstream Records served as a hub for jazz and soul musicians who never quite achieved mainstream prominence. That's not a slight on artists like Harold Land and Hadley Caliman; they simply weren't marketed at the same level as their more famous peers. But their music was equally vital, and on Bob Shad's label, they had the creative freedom to execute their vision. That led to some of the most riveting music of the '60s and '70s—even if the larger listening public didn't realize it.
Shad, who oversaw sessions for Charlie "Bird" Parker and produced albums for Janis Joplin and Dinah Washington, was an avid music fan who put the art first. "He said that he knew nothing about music, other than he loved it and he was going to make records and figure it out," Shad's granddaughter Mia Apatow said in 2019. "All he wanted to do was record artists and be in the studio. That's how he spent his life."
To that end, there's no one way into the Mainstream catalog. As of this writing, there are more than 100 albums on its recently created, and still evolving, Bandcamp page. From the aforementioned Land, McRae, and Caliman, to Brit-rock band Nucleus, jazz vocalist Helen Merrill, and drummer/bandleader Roy Haynes, Mainstream was a treasure trove of stellar genre-hopping music that's just as vibrant today. Here's a deeper look at the musicians who made the label an unexpected powerhouse.
Carmen McRae Carmen Mcrae .
McRae was a jazz vocalist who idolized Billie Holiday. "I have always sung at least one song of Billie Holiday's at every show I've done," McRae said in 1979, "because she was the greatest influence in my life so far as singing was concerned." Before McRae found her own voice, she would imitate Holiday as a way to connect with listeners and thus raise her profile. "I thought I was not a very good talent, and I had to feel that what I had to say was going to be appreciated," she told The New York Times.
At 17, McRae met her hero; the encounter changed her life: "She recorded a tune of mine, 'Dream of Life,' in 1939. If Billie Holiday had never existed, I probably wouldn't have, either." Though there were strong vocal similarities, and McRae's timbre brought forth the same pensive mood, the latter also employed a brighter mix of theatrical flair. Across six albums for Mainstream, including a greatest hits collection, McRae revealed herself to be a singular talent who shone brightest on stage. No matter the setting, whether alone with just a bass, piano, and drum set (her setup for 1971's Carmen McRae), or with a full orchestra (her arrangement for 1964's Second To None), McRae's voice—a robust instrument brimming with style and swagger—was always the highlight. By 1968, the brand of soul that she and Holiday proffered was thought to be a relic; rock had become the country's music of choice. Yet on Live & Wailing, McRae's album from that year, she proved herself to be timeless; whatever music she performed was very much of the moment.
Harold Land
Choma (Burn) Harold Land .
Born in 1928, tenor saxophonist Harold Land started playing when he was 16. Five years later, he recorded four tracks for Savoy Records as leader of the Harold Land All-Stars, his first outing as a bandleader. In 1954, Land joined the Clifford Brown-Max Roach Quintet, a group he played with for a year, until he moved back to Los Angeles to be closer to family. In the years that followed, Land would become synonymous with L.A.; still a national talent connected to jazz luminaries, he often employed local musicians and played gigs around the city when he wasn't on the road. As a result, Land was instrumental in forming the city's jazz sound, a cool Afrocentric sound equally indebted to tradition and funk.
By the early '70s, Land's sound broadened to a heavier blend of funk, free jazz, and rock. Look no further than Choma (Burn), one of two albums he released on Mainstream in 1971: the title track is a swarming 10-minute arrangement in which the drums, vibraphone, and piano all bang into each other. The double bass, played by Reggie Johnson, holds it all together, centering a raucous groove from running off the rails. Other work was less aggressive: A New Shade of Blue was an easygoing interplay between Land and a collective that included Billy Hart on drums and James Mtume (of "Juicy Fruit" fame) on congas. Land would go on to release more albums with labels Concord, Muse, and Timeless, but his work on Mainstream shouldn't be forgotten. At a time when jazz musicians were shedding old ways in search of something new, Land anchored himself in a sound that thrived well beyond that era.
Feelin Good Sarah Vaughan .
Vaughan wasn't underrated by any means. Across her splendid career, the New Jersey-born singer and pianist was perhaps best known for her powerful voice and strong poise, which earned her the nicknames "Sassy" and "The Divine One." In the '40s and '50s, she sang jazz standards, and was often mentioned in the same breath as Ella Fitzgerald and Billie Holiday—her voice had that kind of resonance and vibration.
It didn't matter if the songs were her own or not. On A Time In My Life, her 1971 debut for Mainstream, Vaughan covered the likes of John Lennon and Marvin Gaye, adding jazz-inflected spins to "Imagine" and "Inner City Blues (Make Me Wanna Holler)," among other tracks. Her version of Michel Legrand's "Sweet Gingerbread Man" mixes pop and big band elements, while "That's The Way I've Always Heard It Should Be" has a cinematic aura: muted horns and percussion rise and fall throughout; the band, orchestrated by Ernie Wilkins, pushes Vaughan to some of her best work to that point. But if there's a watershed moment for Vaughan on Mainstream, it's the Live In Japan series. Across two volumes—one released in 1973; the other in 1975—she covered standards like "My Funny Valentine," "Over The Window," and "Bye Bye Blackbird," even asking the audience what they wanted to hear. For a talent as transcendent as Vaughan's, she had an uncanny knack of connecting wholly to the music, giving anyone who heard her sing a once-in-a-lifetime experience.
Many Shades of Blue Blue Mitchell .
Nicknamed "Blue" in high school, Richard A. Mitchell was a Miami-born trumpeter who played all kinds of music: jazz, rock, funk, and R&B. His career began in the early 1950's but didn't truly take off until almost 10 years later, when his multifaceted playing style led to gigs with the great Cannonball Adderley, with whom he recorded 1958's Portrait of Cannonball on Riverside Records. From there, he joined the Horace Silver Quintet until 1964; once it broke up, he formed his own quintet with drummer Roy Brooks and pianist Chick Corea, who was still an upstart talent back then. By the late '60s, traditional jazz wasn't as viable in the mainstream marketplace, so Mitchell supplemented his artistry by playing soul and pop sessions; he toured with Ray Charles, and found new footing as a solo act.
By 1971, Mitchell joined Mainstream and released six albums between 1971 and 1974, sometimes putting out two albums a year. Where his previous work for labels Riverside and Blue Note were overtly jazz, albums like Many Shades of Blue, Blue Mitchell, Vital Blue, Blues' Blues, The Last Tango=Blues, and Graffiti Blues had roots in jazz, but scanned as funk. On those records, Mitchell's horn was front and center, guiding the ambidextrous rhythms without weighing them down. He wasn't going to adhere to rules: Vital Blue, for instance, was an all-encompassing LP that merged bebop and '70s fusion, trekking through nostalgic jazz ("Booty Shakin" and "Vital Blue") en route to something more spiritual (the bossa nova-infused "I Love You") and sensual ("For All We Know"). Graffiti Blues might be his highlight, though. That's where he eschewed any notion of jazz for the sort of rhythmic funk that made Sly & The Family Stone global stars.
Buddy Terry
Pure Dynamite Buddy Terry .
One could argue that the three albums Terry released on Mainstream rivaled anything released on any jazz label, period. Exhibit A: Awareness, a sauntering funk and spiritual jazz LP that pivots between various styles of Black music without leaning too heavily into any one of them. And that's just the opening suite. Throughout his career, the New Jersey-born saxophonist also played with Horace Silver and Ray Charles, but on albums like Awareness, as well as 1972's gospel-centered Lean On Him and Pure Dynamite, Terry cemented his stature as a bonafide leader, not just a sideman or session player meant to facilitate someone else's art. To my ears, Pure Dynamite is the strongest of the set, a fluid mix of Afrocentric jazz that should sit alongside the classics of that era.
Following his Mainstream run, Terry featured on albums with Art Blakey and The Jazz Messengers (Child's Dance on Prestige in 1972) and played jazz fusion with Alphonse Mouzon (The Essence of Mystery on Blue Note in 1973). While he went on to have a fruitful career, there's no denying the sheer virtuosity of his Mainstream work.
Read more in Jazz →


I am roaming internationally with a pre-paid plan (PayGo). I cannot receive any SMS/text from other people (I do get T-mobile operator SMS messages).
How can I ensure that these SMS/text messages are received while I am travelling internationally?
make sure your plan qualifies. And also check the second link for a phone number you can call from anywhere. you may find that support line to be of help.
No, I've got a Samsung Note 4. I have received numerous SMS texts directly from T-Mobile, like "welcome to Singapore" and also a verification code when logging into T-Mobile. But other people have sent me SMS texts and they've not been received.
Hmmm. I don't know if Singapore would require an addition to your plan to work. You should still be able to call Customer Care for free from your device to get more information. Usually all incoming SMS messages are free though.
Free Msg: Unable to send message - Message Blocking is active.
But I checked the settings on my T-Mobile login, and there is no active message blocking.
I called support. They told me to turn on data and allow data roaming, although pre-paid SIM cards technically cannot use data while roaming. I did, and restarted the phone, but still cannot send or receive messages.
Typically prepaid plans do not allow you to roam, which I suspect is the problem. Do you know what rate plan you're on? I'll double check the qualifications.
Prepaid plans, according to the website and prepaid plan details, allow intl roaming. It charges for both incoming and outgoing calls/SMS.
I called Support and they said they couldn't see anything wrong, but looks to me clearly something has blocked the SMS from being sent/received on their network. Calls are coming through fine on the phone while in Singapore.
If you are with Tmobile, please ask them to resolve this. The case was escalated on Saturday, and no response yet.
I'm on the basic prepaid $3 plan. I top up usually with $20 to $40 balances. I only need this SIM to receive SMS and occasional call notifications. I do not need to send any SMS or make phone calls. I got it specifically for this purpose, with the fore-knowledge that I'd be abroad most of the time. I need the SMS receipts for stuff like 2FA logins from many of my accounts.
I think that refers to Canada and Mexico for roaming. I have never beenncharged for incoming texts, even when overseas, but that was back in 2011 when I last went to the UK and took my Sidekick 4G.
I know you mentioned being on the $3 a day plan, do you know if you are on a classic prepaid plan, or can you provide the exact name of your current prepaid plan?
am on the "Pay Go" plan. I just signed up in Nov/Dec, and specifically chose this because it said there was international roaming. I need to receive 2FA SMS codes for logging into my financial services accounts while I am in another country outside USA.
If you are with T-mobile, can you please escalate this? I called last weekend, they said they would escalate, but I have not heard back.
I can NOT receive SMS from anyone else (I just tested again). This is the problem.
My phone shows a signal for both network and data.
Havs you tried entering the APN settings of the carrier you are roaming on to? According to the T-Mobile roaming reddit, the roaming partner for Singapore is Starhub. Here are the Starhub APN settings for SMS/MMS. Do not set it to the default though and reboot your device.
Can you provide the exact name of your plan as you see it on mytmo or on activation paperwork? I know you mentioned pay as you go, unfortunately we have a current version that you can roam with of a similar name, and one you're unable to roam with, also of nearly the same base name.
Did you try the APN settings suggested above?
The apn settings did not help. I tried a few other varieties found online and none impacted the issue.
Apn impacts data usage including mms and internet, so this makes sense why it wouldn't make any changes to getting simple sms to work. I know that prepaid plans like the one I have do not offer data roaming and I'm not needing it.
Please help tech support to open up the network for this to work. I am fine to pay as I go for each sms .. Just needs to work.


Home > Funtasia Too Pricky the Porcupine khaki turtleneck with a porcupine and bird on it with light blue corduroy pants.
Funtasia Too Pricky the Porcupine khaki turtleneck with a porcupine and bird on it with light blue corduroy pants.
Funtasia Too Pricky the Porcupine khaki turtleneck with a porcupine and bird on it with light blue corduroy pants. This is quality and comfortable.


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Multi-Objective Optimizations of Non-Isothermal Simulated Moving Bed Reactor: Parametric Analyses
Revealing of Supercritical Water Gasification Process of Lignin by Reactive Force Field Molecular Dynamics Simulations
Designing and Manufacturing of Automatic Robotic Lawn Mower
Rapid Quality Control of Woodchip Parameters Using a Hand-Held Near Infrared Spectrophotometer
10.3390/pr9020359
Academic Editors
Andrea Pizzi
Giuseppe Toscano
Daniele Duca
Leoni, E.
Mancini, M.
Aminti, G.
Picchi, G.
Open AccessFeature PaperArticle
Wood Fuel Procurement to Bioenergy Facilities: Analysis of Moisture Content Variability and Optimal Sampling Strategy
Elena Leoni
Manuela Mancini
Giovanni Aminti
3 and
Gianni Picchi
3,*
Department of Agricultural, Food and Environmental Sciences, Università Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy
Department of Food Science, University of Copenhagen, Rolighedsvej 26, DK-1958 Frederiksberg, Denmark
CNR-IBE (Institute of Bioeconomy of the National Research Council), via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
Processes 2021, 9(2), 359; https://doi.org/10.3390/pr9020359
Received: 28 January 2021 / Revised: 11 February 2021 / Accepted: 13 February 2021 / Published: 15 February 2021
(This article belongs to the Special Issue Prediction and Monitoring of Biomass Properties for Energy Applications)
Review Reports Versions Notes
Moisture content is the most relevant quality parameter for wood fuels. Effective and fast determination of moisture of incoming feedstock is essential in the management of bioenergy facilities. The availability of fast and reliable moisture meters based on innovative technologies simplifies this task. However, in Mediterranean conditions the inherent variability of wood fuels calls for a careful sampling strategy if representative results are required while facing acceptable analytic costs. The present study is aimed at measuring the fuel heterogeneity and defining accordingly the appropriate number of samples to be analyzed in order to get reliable moisture-content results. A total of 70 truckloads (about 2270 t of woodchips) were sampled during commercial operations in two different seasons. Five samples were collected from each load and measured with standard method and magnetic resonance gauge. Results show that the variability of moisture content is influenced by mixing of species and storage of biomass. Heterogeneity can vary greatly also within single truckloads, to the point that three samples are needed to achieve about 90% of estimates within the desired precision limits. In the case of larger lots, such as barge or ship loads, 20 samples can provide sufficient precision in most scenarios.
sampling; fuel; moisture content; quality; heterogeneity; biomass
The European Union is promoting the substitution of fossil fuels with renewable energy sources with an ambitious plan: by 2030 it aims to reduce at least 40% of greenhouse gas emissions and to cover 27% of energy consumption with renewable energy sources [1]. Even more ambitious is the target to reduce by 2050 the greenhouse gas emissions by 90% as compared to 1990 [2]. These policies have boosted the development of renewable energies. Among those, bioenergy based on woody fuels is still considered one of the main contributors thanks to the availability of a large provision of unused feedstock resources, such as forest logging residues [3] and agricultural residues, industrial and urban waste and, in some contexts, dedicated energy crops [4]. In Italy the number of biomass-fed power plants increased from 75 to 89 in 2012–2013 [5] and is still a thriving sector. Yet, the economic viability and environmental impact of bioenergy production in this country are highly influenced by the great heterogeneity of qualitative properties and energetic characteristics of local wood fuels [6,7].
In this frame, an effective quality control becomes a key factor for the supply chain [8]. Among all quality parameters, Moisture Content (MC) is considered the most relevant [9,10] as it may affect the whole supply chain up to energy conversion. The MC of woody fuels depends on several aspects such as species, inclusion of different tree parts (presence of leaves/needles) and season, among others [11,12]. Furthermore, MC can be influenced by the harvesting and storage operations [13]. The detrimental effects of MC can be highlighted along the whole supply chain [14]. Transport efficiency is penalized by the higher share of moisture over the total payload in truck transportation [15], due to its influence on the bulk density [16]. During yard storage in bioenergy facilities, a high MC promotes microbial activity, which in combination with long storage, large wood-chip piles and adverse weather conditions, may lead to significant dry-matter losses [9]. These can be partially reduced with sheltering systems for the piles, yet this solution increases storage costs and may easily offset the benefits obtained [17]. Moreover, in certain conditions the presence of soluble nutrients and high nitrogen content boosts the microbiological activity [13], which may quickly increase the temperature of biomass. In the worst cases it leads to self-ignition, causing immediate loss of fuel or, at the least, increases the ash content, reducing the overall energy content of the feedstock [18]. As a final drawback, MC is negatively correlated to the heating value, reducing the energy content and the market value of the feedstock [5]. Net calorific value could vary from 6 MJ/kg to 12 MJ/kg in woodchip with a range of moisture content from 2% to 58% [19], while with a mean value of moisture content about 10%, mean gross calorific value is about 18 MJ/kg [20]. In fact, with a high share of water over the dry biomass, the evaporation process subtracts energy from the combustion, reducing the temperature of the process [21]. This causes a non-decrease of the heating value, which drops from 14.07 to 7.82 Mj/kg, respectively, with a moisture content of 20% and 50%. Finally, the lower combustion efficiency caused by a high MC increases the pollutant emissions released with the flue gases, causing a significantly higher environmental impact [22].
Due to the importance of MC in biofuels, it is common practice of bioenergy facilities to set price classes for the incoming biofuel with monetary value inversely proportional to MC. This solution promotes the provision of higher-quality fuel but requires the technical capacity to assess the MC of the incoming loads. Ideally, this measurement should be performed quickly and with a reliable system, allowing the immediate refusal of loads exceeding the agreed threshold. This is hindered by the time required to determine the MC of woody biomass according to the standard method ISO 18134-1:2015 (up to 48 h in oven).
As a response, in the past two decades several producers introduced in the market a number of moisture meters based on diverse technologies and types of sensors. After a setting-up period, the newest MC gauges proved comparable to the standard method in terms of precision and accuracy, but with a much faster production of results and thus capable of handling a higher number of samples in a given time. Nystrom and Dahlquist [23] compared several alternative technologies with the standard oven dry method, concluding that Near Infrared (NIR) spectroscopy and Radio Frequency (RF, also known as dielectric) technologies were the best-suited for MC measurement in flow and bulk fuels, respectively. More recently, further studies confirmed the potential of NIR instruments for MC determination due to their real-time and intuitive approach and the possibility to measure samples without any specific preparation or alteration of the biomass [24], allowing reiterate analysis on the same sample, if required. However, fuel-specific calibration is necessary for correct NIR deployment, making it a less flexible option in conditions of high variability of the biomass characteristics [19]. A further technological option recently introduced among commercial MC analyzers is Magnetic Resonance (MR) [25]. This was successfully tested in industrial environment for quick determination of MC of incoming trucks [22], proving capable to perform about 130 analyses in an 8-h shift and with no sensitivity to the heterogeneity of woody biomass and no need of fuel-specific calibration models.
The latter aspect may be crucial for future developments in the field of industrial MC determination of biofuels. In fact, with high heterogeneity of wood species and/or intrinsic characteristics of the incoming biomass, an improper sampling technique could lead to significant errors even with modern sensors, particularly in the case of large consumers of fuel [18]. In fact, the selection of a limited amount of material to be regarded as physically and chemically representative of large quantities of biomass is a challenging and uncertain operation [26]: in case of inappropriate sampling the whole MC determination process could be biased. This problem is particularly relevant in areas with high heterogeneity of woody fuels, as may be the case with systems based on Mediterranean mixed forests, whose degree of variability is largely unknown.
The technical standard ISO 18135 [27] reports a sampling procedure based on the type and variability of the biofuel, to be assessed beforehand or according to expert assessment, and provides tables to calculate the appropriate sampling intensity. Nevertheless, the application of the guidelines of the standard in commercial biomass sampling showed some critical issues, particularly regarding the excessive demand in terms of time and effort required. Furthermore, the heterogeneity of woody biofuel could be overlooked by the limited availability of consultative tables, which are related just to major biomass typologies. Therefore, guidelines often return a sampling strategy excessively demanding and not linked to the actual uncertainty degree of the MC estimate at the power plant [26].
The possibility to perform reliable analysis in a short time and within the premises of the power plant paves the way to a new approach of MC monitoring of incoming biomass with an increased number of samples per load received and a better description of the fuel characteristics. But this approach requires a deeper understanding of the intrinsic quality variability of woody fuels and the most appropriate sampling procedures to be adopted accordingly.
Against this background the present study aimed at:
better understanding the range of MC variability of incoming biomass in a power plant in real conditions;
relating the MC variability with the reliability of moisture estimate performed on site with a Magnetic Resonance moisture meter (MR-MM), and;
assessing the optimal number of samples to be collected for MC determination (sampling intensity) in the cases of incoming high-volume trucks and of larger lots of woody fuel.
Samples were collected at the fuel yard of a biomass power plant located in a mountain area of Southern Italy in two different sampling periods, hereafter identified as "Summer" and "Autumn". These were selected in order to catch the maximum possible variability in a short time span. Summer sampling was done in early October, when contractors delivered both biomass from ongoing harvest operations (thus with high MC) and material left to dry in intermediate yards during the warm and dry summer. Autumn sampling, performed in late November, involved just wet biomass since dry stocks were depleted and the Mediterranean rainy season had commenced.
The power plant of this study was selected also for the local high variability of tree species and forest treatments that provide the feedstock to the facility. The main forest species locally delivered as wood chips are chestnut, deciduous oaks, poplar, willow and alder among broadleaves; fir, pine and occasionally cypress among conifers. Due to the dominance of mixed forests and the variability of work systems deployed, the biomass can be either mono-specific or a mix of several species. Additionally, procurement can be based on recently felled and comminuted trees, or biomass stored in satellite yards either as logs or loose wood chips. All these factors contribute to the high variability of incoming feedstock and were duly noted as reference data for each truckload in order to analyze their influence on the overall heterogeneity and the reliability of MC fast estimation.
Wood chips were delivered to the power plant with high-volume, moving-floor semitrailers carrying an average payload of 32.4 tonnes (t). A total of 35 trucks were sampled per season, for a total of 70 incoming trucks involved in the study delivering about 2270 t of wood chips. Biomass collection was performed immediately after unloading the wood chips on the paved yard. Five replicates were sampled per pile in predefined positions chosen in order to describe the possible internal variability: three collecting spots were identified on the top and two at the bottom of the heap of wood chips (Figure 1). A manual grain scoop was used to collect the biomass, filling 5 buckets with a capacity of 10 dm3 each.
At the control station of the biomass yard, the content of each bucket was carefully mixed prior to the extraction of 2 subsamples from each sample (Figure 2). The first subsample (hereafter named STAN) had an approximate volume of 3 dm3. It was tipped in a paper bag of known tare and weighed with a precision scale for wet mass determination. Afterwards, all bags were closed and transported to a laboratory to determine the oven dry mass according to standard method (ISO 18134-1:2015). A laboratory scale with a precision of 0.01 g was used for weighing all subsamples.
The second subsample (hereafter named FAST) was collected from the bin using a 0.8 dm3 plastic container, which was directly introduced in the MR–MM for MC determination. Overall, 700 subsamples equally divided between STAN and FAST were collected and analyzed. Further details regarding sampling procedure and the characteristics and performance of the MR–MM can be found in [19].
2.1. Sampling Intensity on Trucks
This action aimed at assessing the MC estimate improvement related to the analysis of an increasing number of samples from the same load (sampling intensity). The average MC of all the five STAN samples collected on each truckload was considered as the reference value (REF_MC). This was compared with the average MC of the FAST samples (MR_MC). The different levels of sampling intensity of MR_MC was simulated using an increasing number of FAST values randomly chosen within the same truckload. For this purpose, a research randomizer [28] was deployed to casually select the MC values to be used for each comparison. The simulated sampling intensity ranged from 1 to 4 FAST values used to calculate the average MC: the combination of the 5 FAST values was not considered since no randomization was possible. In order to avoid random effects, each sampling intensity level (1 to 4) was replicated 5 times with a new set of randomized values. The average values of the 5 replications were used to calculate the error according to the following formula:
δX,I = REF_MCx − MR_MCx,I
"X" is the truckload, ranging from 1 to 70.
"I" is the number of random samples used to calculate the average MC, ranging from 1 to 5.
"REF_MC" is the MC of the truckload X.
"MR_MC" is the average MC of the "I" samples randomly selected among the 5 measured on the truckload X.
2.2. Sampling Intensity on Piles
Similarly to the above procedure, sampling intensity was simulated for large piles of wood chips. The analysis was performed assuming to have two piles composed by the 35 truckloads of each season, thus with an approximate mass of 1130 t. The MC of the piles was calculated with the average value of the 175 STAN samples collected and measured in each season. Estimation of the MC was simulated as the average of a variable number of FAST values. The number of samples used to calculate the average varied according to the assumed sampling intensity: 5, 10, 20 and 30 samples per pile. The simulation was accomplished again with randomization of the FAST values, generating 10 sets of randomly arranged MC values for the two datasets (Summer and Autumn). From these datasets, 50 subsets (replications) of 5, 10, 20 and 30 MC values were randomly selected, and the corresponding average value was calculated. The MC estimation error for the two piles/seasons was calculated according to the following formula:
Δk = REF_MC − MR_MCk
"k" is the number of random samples used to calculate the average MC, with values set to 5, 10, 20 and 30.
"REF_MC" is the MC of the biomass pile.
"MR_MC" is the average MC of the "k" samples randomly selected among the 175 measured on the truckloads composing the pile.
Multifactor ANOVA analysis of MC values was performed to verify normal distribution of data and influence of season and external factors on MC. The five MC values collected in each truckload were used to calculate an average MC value for each sample and the standard deviation of each load (SDt). ANOVA and descriptive analysis were used to compare the values of MC and SDt of trucks and the effect of season and other factors on the variability. An F-test was performed to compare SD of the average MC and SDt values of trucks in different seasons.
3.1. Fuel Quality and Heterogeneity
Moisture analysis of STAN samples confirmed the difference expected between the two sampling seasons. In spite of the limited timespan, the average moisture content rose about 4% in the loads sampled in the Autumn sampling season, reaching almost the 50% threshold (Table 1). It is worth mentioning that companies acted in the frame of a contract setting 45% MC as the target value for the delivered biomass; thus, it is reasonable to assume that the operators made all possible efforts to counter this variation, to be regarded as a consequence of the Mediterranean rainy season. The SD of the whole dataset and the SD of the average MC values of single truckloads (REF_MC) are higher for the Summer period compared to Autumn, even if there is no statistical difference according to the variance check with Levene's test. Accordingly, the range of MC values is much higher in Summer, with a difference of more than 15 percentage points both when considering the whole dataset and the average values of truckloads.
By sorting the truckloads according to the increasing REF_MC value (Figure 3 and Figure 4), it is possible to better visualize the variability recorded among and within the loads. Neither the two sampling seasons nor the REF_MC values show a clear influence on the heterogeneity of the feedstock composing the load. This was confirmed by the F-test comparing SD values of the single trucks (Table 2), where no significant difference was detected among the averages of SD values. This confirms that the internal variability of the biomass carried in each truck did not change with the season and remains very high with a 4.5 range of SD values.
In order to better describe the heterogeneity of the incoming truckloads, SDt values were ranked in unitary SD classes, providing their frequency distribution (Figure 5). Assuming that the desired degree of MC variability for an industrial biomass user falls below an SD value of 2, the threshold is exceeded by 31.4% and 40% of the loads, respectively, for the Summer and Autumn periods.
The analysis of external factors showed that the intermediate storage of biomass prior to transportation has a high impact on SDt values, with a significant increase when biomass is stored in satellite yards before its delivery to the power plant (Figure 6). The species composition did not result in a statistically significant effect on SDt, yet the means plot table shows that pure material either from conifers or broadleaves has higher values, while the mix of both reaches lower SDt and is significantly different from broadleaves according to the multiple-range test.
3.2. Estimate of Moisture Content and Sampling Intensity
3.2.1. Influence of Sampling Point
The comparison of MC values of the FAST samples collected in the five different predefined sampling points showed no significant difference (Figure 7). This result confirms that sampling may be performed on random positions of the biomass pile without compromising the reliability of the procedure. For the purpose of this study, it also endorses the use of random combinations of the five FAST values to estimate the total MC value without incurring procedural bias.
3.2.2. Estimate of Moisture Content in Truckloads
The sampling intensity was first run on the whole dataset comparing REF_MC with MR_MC values with no randomization of the FAST values. These were added in order of sampling. For the analysis of the truckloads was set a threshold of ±2% of tolerated error, considered as the maximum admitted level for the power plant managers. Figure 8 shows the tolerance plots and the percentage of MC estimates with the desired precision according to the sampling intensity used. Single sample estimates provide less than 70% of values with the necessary precision, and four sampling points are necessary to achieve over 90% of estimates within the tolerated error (Table 3).
The MC estimates and the relative δ values of randomized combinations of FAST values provide a more robust insight of the results of an increasing sampling intensity (Table 3). In both sampling seasons, the increment from 1 to 2 FAST samples provides an increase in precision over 10%; 3 FAST samples per load are sufficient to exceed the threshold of 90% of values within the defined limits of precision.
3.2.3. Accuracy of MC Estimation in Piles
Estimation of MC on piles or large lots requires a significant number of samples. However, compared to the MC assessment on truckloads, a higher level of precision can be achieved with a relatively lower number of samples. As shown in Figure 9 and Figure 10, the estimation of MC with an average of 20 samples achieves about 90% of values within the limit of 2% of MC difference. Lower sampling intensity leads to higher errors and is increasingly affected by the inhomogeneity of the MC of wood chips: in Summer, just a tolerance level of ±4 or above allows reducing sampling intensity to 10 FAST samples and still gets more than 90% of observations within the threshold; in Autumn, a tolerance level of ±3 is enough to achieve the same result.
The availability of new instruments for the determination of MC in close-to-real-time at the facility of the biomass end-users provides a unique opportunity to plan an effective control of incoming feedstock, resulting in lower fuel and yard management costs, higher combustion efficiency and, as a consequence, lower emissions. The MC–MM analyzer tested in the study proved to be a reliable and fast instrument for the determination of MC in industrial applications, particularly thanks to the robust measurement system that is not influenced by the quality of biomass and its inherent heterogeneity.
However, in order to fully express the potential of this or comparable instruments for MC determination, it is important to reliably estimate the appropriate number of samples to be collected, i.e., the sampling intensity. This shall be determined as a function of the variability of the feedstock characteristics: a fast assessment and characterization of the incoming biomass is a key strategy to adjust the sampling intensity (and the related costs) with the desired precision of the MC estimate. Given the operative context, the estimate is necessarily based on factors that can be measured or retrieved by the biomass manager for each transport unit received. In fact, this study shows that single truckloads arriving on the same day may differ greatly in terms of internal variability, with about 35% of SDt exceeding the threshold value of 2. According to the results of this study, this variability does not depend on the season or the average MC. This outcome is consistent with other research [29,30] that highlights how, to a certain extent, the inherent and noncontrollable variability of biomass hinders the efficient management of bioenergy facilities. The unique variable with a significant influence is related to the procurement system: storage of biomass. When present, this step of the supply chain increases the heterogeneity of the fuel, making it more difficult to perform an effective estimate of the MC of the unit of fuel delivered (a truckload, a barge, etc.). The importance of its correct evaluation is demonstrated by the significant influence on biomass density, affecting storage and transport conditions [6]. This is probably due to the fact that biomass producers use intermediate storage—generally uncovered—to create a buffer of feedstock, securing the provision to the end user in adverse weather conditions. This is confirmed by several studies on the effect of storage of wood chips [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32], which leads to variations of the fuel properties, particularly if prolonged for several months. Additionally, wood chips from different forest operations are delivered and mixed at the intermediate storage, thus including several species and diverse tree sections. With this process, freshly comminuted material is mixed with "seasoned" biomass already exposed to open-air conditions, leading to a further source of variability.
It is interesting to consider that the heterogeneity observed within the load of single trucks is not related to the variability of the wood chips delivered as a whole (e.g., the daily procurement of a power plant). The case of the two sampling periods in the present study is well representative, presenting a high SDt variability with a relatively limited heterogeneity of piles.
A high and inhomogeneous SDt makes more challenging the estimate of MC of the incoming transport units, with negative consequences on biomass control, unless a high sampling intensity is adopted. MC estimated with a single sample per truck leads to a significant bias, as in the worst case (Autumn) it returns over 30% of values with an error exceeding ±2 percentage points. The adoption of two sampling points significantly enhances the reliability of the estimate, reducing to about 11–14% the values with excessive deviation. The latter can be regarded as a reasonable compromise between cost and precision. When a higher reliability is required, for instance for invoicing purposes and in order to avoid disputes, three samples should be collected in each truckload, with a ratio of about one sample every 10–11 t of feedstock. Such sampling intensity should be adopted also when the biomass manager detects in the load darker and more "seasoned" wood chips coming from intermediate storage. With three samples per truck (i.e., 1 sample every 11 t of biomass), the measuring time required by the MR–MM sets a maximum threshold of about 40–45 trucks measurable per shift—a limit that larger power plants may exceed often and that would require additional shifts or MC sensors, thus further increasing the cost of MC monitoring.
On the contrary, the MC values recorded in the piles composed by the 35 loads are relatively more homogeneous, allowing a reliable estimate of the overall MC even with a lower number of samples. A sampling intensity of 20 samples is sufficient to achieve a probability of 90% to lay below ±2 percentage points of error in MC estimation. This corresponds to a ratio of about one sample every 57 t of feedstock. If ±3 percentage points of error are tolerated, the intensity can be reduced to 10 samples, with a significant cost reduction for the analysis and a ratio of one sample per 114 t of biomass.
Large bioenergy facilities are increasingly adopting fast MC measurement systems in substitution to the slow-responsive thermo-gravimetric method. Among those, Magnetic Resonance systems proved to be particularly reliable, coping with any degree of variability in terms of MC and biofuel properties. As a further benefit, these sensors allow increasing the quantity of samples measured in each load, enhancing the overall quality and reliability of the MC estimate. In order to maximize this opportunity, biomass managers should pay attention to visually evaluating the characteristics of the incoming trucks. In the case of high volume (90 m3) loads with low fuel heterogeneity, two samples per truck (i.e., one sample every 16 t of biomass) may be enough to effectively control MC. However, when dealing with a higher variability (e.g., due to the presence of biomass previously stored in open-air conditions) the results of this study suggest increasing the intensity to three samples per truck (i.e., one sample every 11 t of biomass). When dealing with larger loads, such as barges, an intensity of one sample every 50–60 t of biomass appears to be adequate to correctly estimate the overall MC. The use of tables referring to different types of biofuel in ISO 18135 could be useful to guide biomass managers to define quality parameters thresholds. Yet those should be integrated with further details in order to better support biomass monitoring and quality estimates.
The results of this study relay on a relatively robust database, however further studies, possibly based on yearly records of power plants, should be conducted to better define the thresholds of variability of MC in biomass and the predictable factors influencing it, (e.g., intermediate storage practices). Such information should lay the basis for a new or integrated ISO standard sampling procedure for rapid MC gauges installed within the premises of end users, facilitating a reliable and cost effective MC estimation in commercial operations.
Conceptualization, G.P. and E.L.; methodology, G.P., E.L. and G.A.; formal analysis, G.P., M.M., E.L.; investigation, G.A. and G.P.; data curation, G.P. and E.L.; writing—original draft preparation, E.L. and G.P.; writing—review and editing, G.P., E.L., G.A.; supervision, G.P. All authors have read and agreed to the published version of the manuscript.
The authors wish to acknowledge Alessandro Cinotti for his support in data collection.
Sustainable and Optimal Use of Biomass for Energy in the EU beyond 2020; European Commission: Brussels, Belgium, 2017.
A Roadmap for Moving to a Competitive Low Carbon Economy in 2050; European Commission: Brussels, Belgium, 2011; p. 15.
Nurek, T.; Gendek, A.; Roman, K.; Dabrowska, M. The impact of fractional composition on the mechanical properties of agglomerated logging residues. Sustainability 2020, 12, 6120. [Google Scholar] [CrossRef]
Malico, I.; Nepomuceno Pereira, R.; Gonçalves, A.C.; Sousa, A.M.O. Current status and future perspectives for energy production from solid biomass in the European industry. Renew. Sustain. Energy Rev. 2019, 112, 960–977. [Google Scholar] [CrossRef]
Toscano, G.; Duca, D.; Foppa Pedretti, E.; Pizzi, A.; Rossini, G.; Mengarelli, C.; Mancini, M. Investigation of woodchip quality: Relationship between the most important chemical and physical parameters. Energy 2016, 106, 38–44. [Google Scholar] [CrossRef]
Spinelli, R.; Nati, C.; Sozzi, L.; Magagnotti, N.; Picchi, G. Physical characterization of commercial woodchips on the Italian energy market. Fuel 2011, 90, 2198–2202. [Google Scholar] [CrossRef]
Nurek, T.; Gendek, A.; Roman, K. Forest residues as a renewable source of energy: Elemental composition and physical properties. BioResources 2019, 14, 6–20. [Google Scholar] [CrossRef]
Mancini, M.; Taavitsainen, V.M.; Toscano, G. Comparison of three different classification methods performance for the determination of biofuel quality by means of NIR spectroscopy. J. Chemom. 2019, 33, 1–11. [Google Scholar] [CrossRef]
Krigstin, S.; Wetzel, S. A review of mechanisms responsible for changes to stored woody biomass fuels. Fuel 2016, 175, 75–86. [Google Scholar] [CrossRef]
Pan, P.; McDonald, T.; Fulton, J.; Via, B.; Hung, J. Simultaneous moisture content and mass flow measurements in wood chip flows using coupled dielectric and impact sensors. Sensors 2017, 17, 20. [Google Scholar] [CrossRef][Green Version]
Demirbas, A. Relationships between heating value and lignin, moisture, ash and extractive contents of biomass fuels. Energy Explor. Exploit. 2002, 20, 105–111. [Google Scholar] [CrossRef]
Kulasinski, K.; Guyer, R.; Derome, D.; Carmeliet, J. Water Adsorption in Wood Microfibril-Hemicellulose System: Role of the Crystalline-Amorphous Interface. Biomacromolecules 2015, 16, 2972–2978. [Google Scholar] [CrossRef] [PubMed]
Barontini, M.; Scarfone, A.; Spinelli, R.; Gallucci, F.; Santangelo, E.; Acampora, A.; Jirjis, R.; Civitarese, V.; Pari, L. Storage dynamics and fuel quality of poplar chips. Biomass Bioenergy 2014, 62, 17–25. [Google Scholar] [CrossRef]
Sahoo, K.; Ted Bilek, E.M.; Mani, S. Techno-economic and environmental assessments of storing woodchips and pellets for bioenergy applications. Renew. Sustain. Energy Rev. 2018, 98, 27–39. [Google Scholar] [CrossRef]
Sosa, A.; Acuna, M.; McDonnell, K.; Devlin, G. Controlling moisture content and truck configurations to model and optimise biomass supply chain logistics in Ireland. Appl. Energy 2015, 137, 338–351. [Google Scholar] [CrossRef][Green Version]
Nurek, T.; Gendek, A.; Roman, K.; Dąbrowska, M. The effect of temperature and moisture on the chosen parameters of briquettes made of shredded logging residues. Biomass Bioenergy 2019, 130, 105368. [Google Scholar] [CrossRef]
Therasme, O.; Eisenbies, M.H.; Volk, T.A. Overhead protection increases fuel quality and natural drying of leaf-on woody biomass storage piles. Forests 2019, 10, 390. [Google Scholar] [CrossRef][Green Version]
Hofmann, N.; Mendel, T.; Schulmeyer, F.; Kuptz, D.; Borchert, H.; Hartmann, H. Drying effects and dry matter losses during seasonal storage of spruce wood chips under practical conditions. Biomass Bioenergy 2018, 111, 196–205. [Google Scholar] [CrossRef]
Mancini, M.; Duca, D.; Toscano, G. Laboratory customized online measurements for the prediction of the key-parameters of biomass quality control. J. Near Infrared Spectrosc. 2019, 27, 15–25. [Google Scholar] [CrossRef]
Everard, C.D.; McDonnell, K.P.; Fagan, C.C. Prediction of biomass gross calorific values using visible and near infrared spectroscopy. Biomass Bioenergy 2012, 45, 203–211. [Google Scholar] [CrossRef]
Posom, J.; Shrestha, A.; Saechua, W.; Sirisomboon, P. Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy. Energy 2016, 107, 464–472. [Google Scholar] [CrossRef]
Aminti, G.; Cinotti, A.; Lombardini, C.; Spinelli, R.; Picchi, G. Industrial stress-test of a magnetic resonance moisture meter for woody biomass in southern European conditions. Fuel Process. Technol. 2018, 178, 189–196. [Google Scholar] [CrossRef]
Nyström, J.; Dahlquist, E. Methods for determination of moisture content in woodchips for power plants—A review. Fuel 2004, 83, 773–779. [Google Scholar] [CrossRef]
Toscano, G.; Rinnan, Å.; Pizzi, A.; Mancini, M. The Use of Near-Infrared (NIR) Spectroscopy and Principal Component Analysis (PCA) To Discriminate Bark and Wood of the Most Common Species of the Pellet Sector. Energy Fuels 2017, 31, 2814–2821. [Google Scholar] [CrossRef]
Fridh, L.; Volpé, S.; Eliasson, L. An accurate and fast method for moisture content determination. Int. J. For. E 2014, 25, 222–228. [Google Scholar] [CrossRef]
Toscano, G.; Leoni, E.; Feliciangeli, G.; Duca, D.; Mancini, M. Application of ISO standards on sampling and effects on the quality assessment of solid biofuel employed in a real power plant. Fuel 2020, 278, 118142. [Google Scholar] [CrossRef]
ISO 18135:2017 Solid Biofuel—Sampling; ISO: Geneva, Switzerland, 2017; p. 66.
Urbaniak, G.C.; Pluos, S. Research Randomizer (Version 4.0). Available online: www.randomizer.org (accessed on 11 June 2020).
Nunes, L.J.R.; Godina, R.; Matias, J.C.O.; Catalão, J.P.S. Evaluation of the utilization of woodchips as fuel for industrial boilers. J. Clean. Prod. 2019, 223, 270–277. [Google Scholar] [CrossRef]
Kuptz, D.; Schreiber, K.; Schulmeyer, F.; Lesche, S.; Zeng, T.; Ahrens, F.; Zelinski, V.; Schön, C.; Pollex, A.; Borchert, H.; et al. Evaluation of combined screening and drying steps for the improvement of the fuel quality of forest residue wood chips—results from six case studies. Biomass Convers. Biorefinery 2019, 9, 83–98. [Google Scholar] [CrossRef]
Manzone, M.; Balsari, P. Poplar woodchip storage in small and medium piles with different forms, densities and volumes. Biomass Bioenergy 2016, 87, 162–168. [Google Scholar] [CrossRef][Green Version]
Casal, M.D.; Gil, M.V.; Pevida, C.; Rubiera, F.; Pis, J.J. Influence of storage time on the quality and combustion behaviour of pine woodchips. Energy 2010, 35, 3066–3071. [Google Scholar] [CrossRef][Green Version]
Figure 1. Location of the predefined sampling areas in the pile of unloaded wood chips (side view).
Figure 2. Sampling strategy for the selection of 5 first subsamples (STAN) and 5 s subsamples (FAST) from each incoming truck.
Figure 3. Average MC values of truckloads in increasing order for Summer sampling period. The red line represents the average MC value (44.3%), black bars show the SD.
Figure 4. Average MC values of truckloads in increasing order for Autumn sampling period. The red line represents the average MC value (49.2%), black bars show the SD.
Figure 5. Classes of SD values of the loads measured in the two sampling seasons.
Figure 6. Means plot of the influence of storage (a) and species (b) factors over SDt values.
Figure 7. Box-plot of the five sampling points (as in Figure 1). The box encloses 50% of observations, the vertical line reports the median value and "+" indicates the average value.
Figure 8. Tolerance plots of the fitted normal distribution representing the frequency of δ values (deviation of MC estimate). The central red line indicates the center of the normal distribution (exact matching). Outer red lines indicate the threshold admitted of ±2 MC percentage points of error. The percentage of values within the limits is reported below each plot.
Figure 9. Sensitive analysis of MC estimation accuracy of the Summer pile based on the average of an increasing number of samples (5, 10, 20 and 30). Trend lines show the result according to different error tolerance levels.
Figure 10. Sensitive analysis of MC estimation accuracy of the Autumn pile based on the average of an increasing number of samples (5, 10, 20 and 30). Trend lines show the result according to different error tolerance levels.
Table 1. Moisture content (MC) of all samples for the two seasons. Values in column with different letters (a and b) show a statistical difference with p-Value below 0.05.
Sampling Season
Avg. Moisture Content (MC%)
Coefficient of Variation (%)
Minimum Value (MC%)
Maximum Value (MC%)
Range (MC% Points)
Summer 44.5 a 6.01 13.51 24.9 60.9 36.0
Autumn 49.2 b 4.88 9.91 39.4 58.8 19.4
AVERAGE VALUE OF TRUCKS
Summer 44.5 a 5.84 13.1 27.2 56.3 29.1
Table 2. Analysis of the SD values of each trucks (relative to the 5 samples measured in each truck).
Count 35 35
Average 1.94 2.08
Standard deviation 1.21 1.09
Coeff. of variation 62.2% 52.7%
Minimum 0.51 0.48
Maximum 5.03 4.88
Range 4.52 4.40
Table 3. Percentage of δ values below the threshold of ±2 percentage points of error for the different levels of sampling intensity.
Sampling Intensity
1 74.3% 62.9%
Leoni, E.; Mancini, M.; Aminti, G.; Picchi, G. Wood Fuel Procurement to Bioenergy Facilities: Analysis of Moisture Content Variability and Optimal Sampling Strategy. Processes 2021, 9, 359. https://doi.org/10.3390/pr9020359
Leoni E, Mancini M, Aminti G, Picchi G. Wood Fuel Procurement to Bioenergy Facilities: Analysis of Moisture Content Variability and Optimal Sampling Strategy. Processes. 2021; 9(2):359. https://doi.org/10.3390/pr9020359
Leoni, Elena, Manuela Mancini, Giovanni Aminti, and Gianni Picchi. 2021. "Wood Fuel Procurement to Bioenergy Facilities: Analysis of Moisture Content Variability and Optimal Sampling Strategy" Processes 9, no. 2: 359. https://doi.org/10.3390/pr9020359
Processes, EISSN 2227-9717, Published by MDPI


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text:
Local landscaping company gives back to front line health care workers
by: Jaime Travers
ST. LOUIS – Growing Green Incorporated, a local plant-scaping company, is giving out hundreds of hanging flower baskets to SLU Hospital and Cardinal Glennon's health care heroes.
On Thursday morning, Bommarito Automotive SkyFox flew over SLU Hospital while the flowers were being handed out. The Growing Green employees arranged the baskets of pansies, donated by N.G Heimos Greenhouses, in a huge flower design in the parking lot.
These flowers were a beautiful way to say thanks to those employees who are on the front lines of this pandemic in the St. Louis region.
Some of the hospital staff were just coming off of night shifts and were able to get their flowers and take them home immediately. Others were greeted by the beautiful flowers as their shifts were just starting.
The St. Louis community has shown an outpouring of love and support for our health care workers. SLU Hospital employees have also received handmade thank you notes for workers and their families who are supporting them as well as donations of food and homemade masks.
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by Chris Hayes / Jan 20, 2021
LINCOLN COUNTY, Mo. – In a 7-month FOX 2 investigation, we've followed the trail of a death reported to be COVID that turned out not to be. The victim's son reached out to FOX 2 in June 2020.
The Missouri Department of Health and Senior Services now acknowledges this death certificate was filled out in error. It lists a cause of death as COVID when it wasn't.
by Andy Banker, Staff Writer / Jan 20, 2021
OAKVILLE, Mo. – Parents are demanding action from administrators at Oakville High School after a senior wore a Confederate flag face mask to school. A parent tells FOX 2 a student showed up to school wearing the Confederate flag mask and was allowed to continue through his school day. It was only after students began texting their parents and parents contacted the school that administrators started looking into it.
"So what type of message are you sending to my child by allowing another child to sit in the classroom with this mask on, said Tamica Harding.
JEFFERSON CITY, Mo. (AP) — Republican senators in Missouri are trying to check health officials' power in response to their handling of the coronavirus pandemic.
Senate health committee members debated a bill Wednesday that would ensure there are no government-imposed occupancy limits on churches and other places of worship during public health emergencies.
More local COVID-19 maps and stats


text:
Красноарме́йский — бывший посёлок городского типа в Чаунском районе Чукотского автономного округа России.

Расположен в отрогах Шелагского хребта, на берегу реки Пырканайваам, в 100 км восточнее Певека, с которым связан круглогодичной автодорогой.

Название
Название Пырӄаӄай происходит от пырӄа «выветренная скала», «каменный столб» и «кекур», «-ӄэй/-ӄай» — уменьшительный суффикс).

Население

История
Основан 31 марта 1940 г. при оловянном прииске «Пыркакай». В честь побед Красной Армии 9 января 1942 г. посёлку было присвоено современное название.

Статус посёлка городского типа — с 1953 года.

В 1998 году ликвидирован.

Улицы
 Улица Гагарина
 Улица Геологов
 Улица Ерофеева
 Улица Ленина
 Улица Мандрикова
 Улица Полярная
 Улица Пыркакай
 Улица Советская
 Улица Спортивная
 Улица Строителей

Примечания

Ссылки
 Форум бывших жителей посёлка
 terrus.ru — база данных России
 Красноармейский на dead-cities.ru

Исчезнувшие населённые пункты Чукотского автономного округа
Населённые пункты Чаунского района
Бывшие посёлки городского типа Чукотского автономного округа

redpajama_set_name: RedPajamaWikipedia


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The final function all terrain tire normally has an interlocked tread pattern with siping or small cuts on the tread blocks. Also, the hollows in these tires are typically a lot smaller than these on tires meant for the mud. The denser sample of blocks and smaller voids provides less noise to those tires on the street. Moreover, it also augments the surface space of the tread which gives the tire enhanced flotation on surfaces like gentle powdery snow or sand.