Optimizing Waste for Energy: Exploring Municipal Solid Waste Variations on Torrefaction and Biochar Production

International Journal of Energy Research, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Energy primarily comes from fossil fuels, which leads to environmental deterioration through increased carbon dioxide load and other greenhouse gases in the atmosphere. Renewable energy is a cheap alternative, biomass, like municipal solid wastes (MSWs), can be suitably used for production. This paper reviews impact of variations MSW composition on its physical, chemical, lignocellulosic properties. It further illustrates how these properties affect torrefaction products. was observed that refer either combination different waste types or independent wastes; hence, there no standard MSW. The are responsible fluctuating These properties, along with process parameters, simultaneously torrefied product, whereas influence biochar yield, physical chemical calorific value ash content. Torrefying containing low moisture content yields high value. Methods improve have not been studied. Research needed assess possibility improving by enhancing lignin percentages, possibly blending A guide best blend combinations ratios required. Also, it crucial study optimal parameters.

Language: Английский

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Comprehensive thermal properties, kinetic, and thermodynamic analyses of biomass waste pyrolysis via TGA and coats-redfern methodologies

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: unknown, P. 100723 - 100723

Published: Sept. 1, 2024

Language: Английский

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An ensemble multi-ANN approach for virtual oxygen sensing and air leakage prediction in biomass gasification plants

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122376 - 122376

Published: Jan. 1, 2025

Language: Английский

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Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1200 - 1200

Published: Feb. 28, 2025

Artificial intelligence (AI), particularly supervised machine learning, has revolutionized the biofuel industry by enhancing feedstock selection, predicting fluid compositions, optimizing operations, and streamlining decision-making. These algorithms outperform traditional models accurately handling complex, high-dimensional data more efficiently cost-effectively. This study assesses effectiveness of various learning in engineering, focusing on a comparative analysis artificial neural networks (ANNs), support vector machines (SVMs), tree-based models, regularized regression models. The results show that random forest (RF) excel syngas composition its lower heating value (LHV), achieving high precision with training testing RMSE values below 0.2 R-squared close to 1. A detailed SHAP identified steam-to-biomass ratio (SBR) as most critical factor these predictions while also noting significant impact temperature conditions. underscores importance thermal parameters gasification supports systematic integration AI production enhance predictive accuracy.

Language: Английский

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Gasification process prediction using a novel and reliable metaheuristic algorithm coupled with the K-nearest neighbors

Chemical Product and Process Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Abstract The present work introduces a new method for forecasting the formation of CH 4 and C 2 H n gases in gasification biomass. K-nearest neighbors (KNN) algorithm is utilized as base model, while two innovative optimization techniques, Artificial Rabbits Optimization (ARO) Smell Agent (SAO), are employed to enhance overall performance achieve optimal results. goal this investigation create prediction model that can reliably accurately anticipate amount produced during By combining strengths KNN with capabilities ARO SAO, proposed approach aims overcome existing limitations process predictions. experimental results demonstrate effectiveness combined predicting estimating quantities produced. integration SAO enables better leading improved accuracy reliability outcomes. Additionally, suggested models was thoroughly evaluated assessed utilizing evaluators. Remarkably, KNSA (combination SAO) achieved highest R values 0.994 0.995 , correspondingly, which demonstrates methods. conclusion study contributes field biomass gasification, it methodology used further improving its through implementation techniques. Further optimizations may now be opened, set insights derived from research curiosity-driven scholars practitioners renewable energy production.

Language: Английский

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Process simulation and BPNNM prediction for chemical looping co-gasification of rice husk and textile wastes as cement alternative fuels

Biomass Conversion and Biorefinery, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

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Fuzzy Neural Network Applications in Biomass Gasification and Pyrolysis for Biofuel Production: A Review

Energies, Journal Year: 2024, Volume and Issue: 18(1), P. 16 - 16

Published: Dec. 24, 2024

The increasing demand for sustainable energy has spurred interest in biofuels as a renewable alternative to fossil fuels. Biomass gasification and pyrolysis are two prominent thermochemical conversion processes biofuel production. While these effective, they often influenced by complex, nonlinear, uncertain factors, making optimization prediction challenging. This study highlights the application of fuzzy neural networks (FNNs)—a hybrid approach that integrates strengths logic networks—as novel tool address challenges. Unlike traditional methods, FNNs offer enhanced adaptability accuracy modeling nonlinear systems, them uniquely suited biomass processes. review not only ability optimize predict performance but also identifies their role advancing decision-making frameworks. Key challenges, benefits, future research opportunities explored, showcasing transformative potential

Language: Английский

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Artificial intelligence in plastic recycling and conversion: A review

Resources Conservation and Recycling, Journal Year: 2024, Volume and Issue: 215, P. 108090 - 108090

Published: Dec. 18, 2024

Language: Английский

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