Co-pyrolysis of municipal solid waste and rice husk gasification tar to prepare biochar: An optimization study using response surface methodology

Gan, Ziyu; Zhuang, Xiaozhuang; Cen, Kehui; Ba, Yuping; Chen, Dengyu

doi:10.1016/j.fuel.2022.125574

Cited by 29 publications

(5 citation statements)

References 47 publications

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“…First, the notion of sustainable biomass is central because it guarantees that the source is consistent with environmentally acceptable practices. This consideration relates to environmental concerns and the ethical implications of biochar generation 59,60 . Second, the feedstock employed has a considerable impact on the characteristics of the resulting biochar.…”

Section: Biochar Production and Applicationsmentioning

confidence: 99%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine‐learning‐based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far‐reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data‐driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

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Section: Biochar Production and Applicationsmentioning

confidence: 99%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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“…Improving the quality of pyrolysis products, e.g., oil efficiency, reducing water and oxygen content, and increasing the caloric value of oil production is important (de Rezende Locatel et al 2022). Co-pyrolysis is a process that involves more than one material as feedstock (Gan et al 2022;Wang et al 2021). During Co-pyrolysis, disparate different sorts of plastic such as Polypropylene (PP), Low-density polyethylene (LDPE), High-density polyethylene (HDPE), Polyvinyl chloride (PVC), Polystyrene (PS), and PET are mixed with sewage sludge, on-edible seeds, agricultural waste, wood waste, and microalgae that improve the quality of pyrolysis product (Kumar Mishra and Mohanty 2020;Miskolczi 2013;Su et al 2022).…”

Section: Chemical Graphitization Of Graphene Oxidementioning

confidence: 99%

Enhancing Face Recognition Performance: A Comprehensive Evaluation of Deep Learning Models and a Novel Ensemble Approach with Hyperparameter Tuning

S.N

et al. 2024

Preprint

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In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study leverages five pre-trained CNN models, including DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception, for feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, optimized through a grid search technique, is introduced to enhance feature extraction and classification performance. Robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, is emphasized to ensure reliable FR systems. The research systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations are conducted on diverse datasets, including ORL, GTAV, GTF, FEI, LFW, F_LFW, and YTF, to assess the effectiveness of the proposed models. Key contributions of this work include the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the use of comprehensive evaluation metrics. The results showcase the superior performance of the proposed method, consistently outperforming all other models across key metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Receiver Operator Characteristic (ROC) curves further highlight the models' classification abilities. Notably, the proposed method achieves an exceptional accuracy of 99.48% on the LFW dataset, surpassing state-of-the-art benchmarks. In conclusion, this research presents a significant advancement in FR technology, offering a reliable and accurate solution supported by empirical evidence. The proposed method demonstrates the potential of pre-trained CNN models, ensemble learning, robust data pre-processing, and hyperparameter tuning in enhancing the accuracy and reliability of FR systems, with implications for various real-world applications.

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“…Based on the literature review, the engine distinctiveness is investigated and optimized using RSM, ANN, Fuzzy and TOPSIS methods only. 5,18,20,23,25,28,29 As per our knowledge, none of the report demonstrates the co-pyrolysis of AM and AS and no one reported the usage of COPRAS and GRA methods in engine characteristics optimization. For the first time, co-pyrolysis of Annona squamosa seed particles and Aegle marmelos pressed seed cakes has been performed/select a new feedstock in this research and hybrid-optimization of the engine control factors has been done using COPRAS and GRA models to maximize the engine performance and minimize the emission.…”

mentioning

confidence: 93%

“…The important objective of this study was to characterize the co‐pyrolysis oil obtained from thermochemical conversion of AS seed particles and AM de‐oiled cake and to expose the appropriateness for commercial engine applications. Based on the literature review, the engine distinctiveness is investigated and optimized using RSM, ANN, Fuzzy and TOPSIS methods only 5,18,20,23,25,28,29 . As per our knowledge, none of the report demonstrates the co‐pyrolysis of AM and AS and no one reported the usage of COPRAS and GRA methods in engine characteristics optimization.…”

Section: Introductionmentioning

confidence: 99%

A COmplex PRoportional ASsessment and Gray Relational Analysis methodology hybrid optimization of compression ignition engine distinctiveness fuelled by dual feed stock pyrolysis of Annona squamosa seed particles and Aegle marmelos pressed seed cake pyrolytic liquid‐diesel opus

Raju¹,

Paramasivam

2023

Env Prog and Sustain Energy

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The deficiency of fossil fuel resources, amplifying fuel price and its related environmental anxieties grasped great attention for finding an alternative fuel. Such alternative options can be fuels obtained from agricultural residues, plants, and wastes. The present research emphases on complex proportional assessment (COPRAS) and gray relational analysis (GRA) modeling of a diesel engine fuelled by Annona squamosa (AS) and Aegle marmelos (AM) co‐pyrolysis oil/diesel binary opus as an engine fuel to optimize the engine characteristics. Co‐pyrolysis oil is obtained from AS+AM feedstock at pyrolysis temperature of 500°C. To obtain engine data for training and testing the proposed COPRAS and GRA models were adopted for the engine control factors such as engine load (W) and compression ratio (CR) in a test engine rig through experimentation and hybrid optimization technics. From the engine output responses reveals that F2080 opus delivers an augmented brake thermal efficiency (22.01%) than diesel (21.56%) at peak load. Subsequently, F2080 fuel emits substantial lessening of CO (11.88%) and HC (5.64%) emissions as compared to diesel (F0). Based on the results, it is conclude that F2080 blend at CR of 17.5 and 100% load was delivered the greater engine distinctiveness through optimization outcomes and experimental evaluation compared to F0.

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Co-pyrolysis of municipal solid waste and rice husk gasification tar to prepare biochar: An optimization study using response surface methodology

Cited by 29 publications

References 47 publications

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Enhancing Face Recognition Performance: A Comprehensive Evaluation of Deep Learning Models and a Novel Ensemble Approach with Hyperparameter Tuning

A COmplex PRoportional ASsessment and Gray Relational Analysis methodology hybrid optimization of compression ignition engine distinctiveness fuelled by dual feed stock pyrolysis of Annona squamosa seed particles and Aegle marmelos pressed seed cake pyrolytic liquid‐diesel opus

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