Methods for determination of moisture content in woodchips for power plants—a review

Nyström, Jenny; Dahlquist, Erik

doi:10.1016/j.fuel.2003.11.002

Cited by 105 publications

(49 citation statements)

References 8 publications

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“…The reason is that water does not participate in the reaction itself and only absorbs heat energy as it evaporates (Nyström and Dahlquist 2004), biasing the results of analyses in favor of drier fuels. The gross calorific value refers to the heat released from fuel combustion with the original and generated water in a condensed state.…”

Section: Resultsmentioning

confidence: 99%

Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

et al. 2017

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Biomass utilization is vital for developing sustainability in the bioenergy sector. In this work the effects of high ash content on the heating properties of wood chips were evaluated. In an analysis of 450 wood chips samples, the ash content, moisture content, and gross calorific value were determined, and a generalized linear model was created to identify the relationship between the gross calorific value and the ash content of the wood chips. The mean ash content of the analyzed wood chips samples was 2.64%, the mean moisture content was 38.8%, and the mean gross calorific value was 19.43 MJ kg -1. Statistical analyses showed that 49% of the gross calorific value variability was due to the ash content variability. A one percent increase in ash content resulted in a 0.11 MJ kg -1 decrease of gross calorific value. The estimated costs of ash disposal at various ash contents were calculated. Burning wood chips with 5% ash content would lead to depositing an extra 5.6 megatons in the US or 21.2 megatons in the EU, compared to burning wood chips with 2.5% ash content.

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Section: Resultsmentioning

confidence: 99%

Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

et al. 2017

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“…The direct measurements include the use of dual-energy X-ray absorptiometry (DXA) (Nordell and Vikterlöf, 2000), near infrared spectroscopy (NIR) (Axrup et al, 2000), radio frequency (RF), microwave (Okamura and Zhang, 2000), and nuclear magnetic resonance (NMR) (Rosenberg et al, 2001) (Nyström and Dahlquist, 2004). Among these methods, NIR has been investigated the most as a promising method to analyze the fuel moisture content either by an automatic sampling on the delivery or in the fuel mix before it is injected into the furnace.…”

Section: Methods For Determination Of the Moisture Content In Biomassmentioning

confidence: 99%

“…Measuring the moisture content of the fuel is carried out on-line with direct and indirect methods. In indirect methods -that measure gas moisture content -the fuel moisture content can be derived by a mass balance, including the moisture content of the combustion air, the elementary composition of the fuel, and the composition of the combustion air (Nyström and Dahlquist, 2004). This time delay due to the transport time of the gas from the furnace to the measurement position can be measured within seconds, which broadens the possibility for successful disturbance compensation by the plant control (Hermansson et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Model predictive control utilizing fuel and moisture soft-sensors for the BioPower 5 combined heat and power (CHP) plant

Kortela¹,

Jämsä‐Jounela²

2014

Applied Energy

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Climate change and environmental concerns are forcing process industries to increase the share of sustainable resources in energy production. The utilization of biomass receives an increasing attention as a replacement for fossil fuels due to its wide availability and sustainability. However, the unpredictable variability of biomass properties, including moisture content, composition and heating value, results in disturbances, faults, and failures during the power plant operation, which creates additional barriers for a wider utilization of biomass.This thesis focusses on the development of a fault tolerant model predictive control (FTMPC) scheme that addresses the challenges associated with the biomass utilization for power production in BioGrate boilers. The novelty of this scheme lies in the integration of softsensors measuring the unpredictable biomass properties with a fault accommodation mechanism.The effectiveness of the developed FTMPC scheme is successfully tested with a dynamic simulator of the BioGrate boiler. This simulator is constructed using the industrial test data from the BioPower 5 CHP plant. In addition, industrial tests, conducted to evaluate the performance of the developed soft-sensors, confirm the prediction accuracy of the fuel moisture content and combustion power in the furnace. Subsequently, the economic evaluation of the soft-sensors integrated FTMPC scheme is presented. Tiivistelmä Ilmastonmuutos ja ympäristökysymykset pakottavat prosessiteollisuutta kasvattamaan kestävien luonnonvarojen osuutta energiantuotannossa. Biomassa saa kasvavaa huomiota korvaavana vaihtoehtona fossiilisille polttoaineaineille johtuen sen hyvästä saatavuudesta ja hiilineutraalisuudesta. Kuitenkin biomassan ominaisuuksien arvaamaton vaihtelu, mukaan lukien kosteus, koostumus ja lämpöarvo, johtaa häiriöihin, vikoihin ja toimintahäiriöihin laitoksen operoinnissa, mikä luo esteitä biomassan laajemmalle hyödyntämiselle.Tämä väitöskirja keskittyy vikasietoisen malliprediktiivisen säätö (FTMPC) -järjestelmän kehittämiseen ottaen huomioon haasteet biomassan hyödyntämisessä energiantuotannossa BioGrate-kattiloilla. Uutta tässä järjestelmässä on soft-sensorien integrointi mittaamaan biomassan vaihtelevia ominaisuuksia yhdessä vikasietoisen menetelmän kanssa.Kehitetyn FTMPC-järjestelmän tehokkuus on testattu käyttäen BioGrate-kattilan dynaamista simulaattoria, joka on tehty käyttäen hyväksi BioPower 5 CHP -laitoksen teollisia mittauksia. Lisäksi, teollisuudessa suoritetut koetulokset, joita käytettiin kehitettyjen softsensorien suorituskyvyn arvioimiseen vahvistavat, että polttoaineen kosteus ja palamisteho voidaan ennustaa hyvällä tarkkuudella. Tämän lisäksi kehitetyn FTMPC-järjestelmän taloudellinen merkitys on arvioitu väitöskirjassa.Avainsanat vikasietoinen säätö, malliprediktiivinen säätö, biomassa, polttoaineen laatu , polttoaineen kosteus, teollinen prosessi, palaminen ISBN (painettu)

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“…Conductivity is an important physical parameter of solid, liquid and organism, which has been widely used in chemical industry, metallurgy, biology, medicine, food and water and other production and scientific research departments [1][2][3][4]. According to the relative position detection element and the measured solution conductivity measurement is divided into non-contact measurement and contact measurement in two categories.…”

Section: Introductionmentioning

confidence: 99%

A Novel Resistance Measurement Method Based on Double Frequency Excitation Signals Eliminating the Infection of Distributed Capacitance

Zhong¹,

Zhao²

2017

IJCA

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Methods for determination of moisture content in woodchips for power plants—a review

Cited by 105 publications

References 8 publications

Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

Model predictive control utilizing fuel and moisture soft-sensors for the BioPower 5 combined heat and power (CHP) plant

A Novel Resistance Measurement Method Based on Double Frequency Excitation Signals Eliminating the Infection of Distributed Capacitance

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