Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR)

Yatim, Fatima Ezzahra; Boumanchar, Imane; Srhir, Bousalham; Chhiti, Younes; Jama, Charafeddine; Alaoui, Fatima Ezzahrae M’hamdi

doi:10.1016/j.wasman.2022.09.013

Cited by 30 publications

(12 citation statements)

References 47 publications

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“…This approach aligns well with the concept of the circular economy, whose strategy is defined in the ISO 14044 standard [18,19]. It should be emphasized that policies aimed towards polluted biomass are becoming increasingly restrictive.…”

Section: Introductionmentioning

confidence: 56%

Calorific Value of Zea mays Biomass Derived from Soil Contaminated with Chromium (VI) Disrupting the Soil’s Biochemical Properties

et al. 2023

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One of the major challenges faced by contemporary agriculture is how to achieve better yields of crops and, consequently, higher biomass, even in unfavorable environmental conditions. This challenge corresponds to the assumptions of sustainable development, wherein it is envisaged that plant biomass should be used on a large scale for heat generation or conversion of biofuels. Keeping pace with observed trends, the following study was conducted in order to determine the effect of Cr(VI) on the net calorific value of Zea mays, to assess the impact of this element on soil enzymatic activity, and to identify the effectiveness of compost and humic acids in alleviating possible negative effects of Cr(VI) toxicity. These aims were pursued by setting up a pot experiment, in which soil either uncontaminated or contaminated with increasing doses of Cr(VI) of 0, 15, 30, 45, and 60 mg Cr kg−1 d.m. was submitted to biostimulation with compost and the preparation HumiAgra, a source of humic acids, and cropped with Zea mays. The plant height, yield, and net calorific value of the aerial parts of maize, as well as its root yield, were determined. Additionally, the activity of seven soil enzymes and the values of the impact indices of compost and HumiAgra relative to the analyzed parameters were determined. It was found that Cr(VI) decreased the amount of energy obtained from the plants by decreasing maize biomass, and additionally by distorting the biochemical balance of the soil. Dehydrogenases, urease, and arylsulfatase proved to be particularly sensitive to this element. It was demonstrated that HumiAgra was more effective than compost in mollifying the adverse effects of Cr(VI) on the activity of soil enzymes and, consequently, on the biomass of Zea mays.

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Section: Introductionmentioning

confidence: 56%

Calorific Value of Zea mays Biomass Derived from Soil Contaminated with Chromium (VI) Disrupting the Soil’s Biochemical Properties

et al. 2023

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“…Neural networks are mathematical models of stochastic nature composed of units or nodes called neurons that can predict system outputs with high precision, low cost, and short processing time [50]. They are based on two stages: training and validation.…”

Section: Neural Networkmentioning

confidence: 99%

“…The linear transfer functions (Purelin and Poslin), Log-Sigmoid function (Logsig), and Tan-Sigmoid function (Tansig) are often reported in the literature. To train the network and obtain the best weights of neurons, algorithms such as quasi-Newton (QN), sealed conjugate (SC) and Levenberg-Marquardt (LM) are cited by Yatim et al [50] and Güleç et al [52].…”

Section: Neural Networkmentioning

confidence: 99%

“…Neural networks can be applied in the context of seasonal variability in the composition and supply of biomass for production chain situations in which the volume of data enables computational algorithms to learn from varied examples. According to Yatim et al [50], artificial neural networks are one of the most applicable and widely used algorithms in the field of waste-to-energy (combustion) design and optimization as a tool for higher heating value (HHV) estimation based on ultimate analysis. The correlations developed make the process cheaper by consuming less time and equipment.…”

Section: Neural Networkmentioning

confidence: 99%

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Tools for Optimization of Biomass-to-Energy Conversion Processes

et al. 2023

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Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conversion as general components, can be costly and uncertain due to inherent variability. Optimization methods are widely applied for modeling the biomass supply chain (BSC) for energy processes. In this qualitative review, the main aspects and global trends of using geographic information systems (GISs), linear programming (LP) and neural networks to optimize the BSC are presented. Modeling objectives and factors considered in studies published in the last 25 years are reviewed, enabling a broad overview of the BSC to support decisions at strategic, tactical and operational levels. Combined techniques have been used for different purposes: GISs for spatial analyses of biomass; neural networks for higher heating value (HHV) correlations; and linear programming and its variations for achieving objectives in general, such as costs and emissions reduction. This study reinforces the progress evidenced in the literature and envisions the increasing inclusion of socio-environmental criteria as a challenge in future modeling efforts.

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“…There are several studies for the estimation parameters of a system us-ing machine learning methods such as Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGB) [18][19][20][21][22][23][24]. In one of these studies, Barman et al [18] represented a chlorophyll meter which can estimate the chlorophyll of citrus leaf by using a smartphone image.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Three-Phase Induction Motor Efficiency at Different Loads

Ileri

Dalcalı

Durgut

2023

Preprint

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Asynchronous machines are widely used in many industrial applications due to their high reliability and low cost. One of the important parameters determining motor performance is efficiency which varies under different load conditions. Many different methods can obtain the efficiency of the motor. While most of these methods require direct intervention on the motor, high-accuracy efficiency prediction can be achieved using machine learning methods that do not require intervention. In this study, efficiency prediction was made using the manufacturer data of 470 different motors under three different load conditions such as full load, half load, and 75% load. Four different machine learning methods were applied with different configurations to predict efficiency values with high accuracy. These methods include MultiLayer Perceptron (MLP), Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGB). The highest accuracy was achieved with RF at half load, XGB at full load, and 75% load. In addition, it was determined that IST/IFL and P(kW) were the most influential factors for efficiency at all loads. PACS: 0000, 1111 2000 MSC: 0000, 1111

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Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR)

Cited by 30 publications

References 47 publications

Calorific Value of Zea mays Biomass Derived from Soil Contaminated with Chromium (VI) Disrupting the Soil’s Biochemical Properties

Calorific Value of Zea mays Biomass Derived from Soil Contaminated with Chromium (VI) Disrupting the Soil’s Biochemical Properties

Tools for Optimization of Biomass-to-Energy Conversion Processes

Estimation of Three-Phase Induction Motor Efficiency at Different Loads

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