Multi-objective exergetic optimization of continuous photo-biohydrogen production process using a novel hybrid fuzzy clustering-ranking approach coupled with Radial Basis Function (RBF) neural network

Aghbashlo, Mortaza; Hosseinpour, Soleiman; Tabatabaei, Meisam; Dadak, Ali; Younesi, Habibollah; Najafpour, Ghasem

doi:10.1016/j.ijhydene.2016.08.123

Cited by 26 publications

(4 citation statements)

References 24 publications

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“…Moreover, the RBF neural network has a strong tendency for approximating nonlinear functions 26 . The robust predictability of the hydrogen production from the co‐gasification of waste plastic and rubber using the RBF neural network model is consistent with that reported by Aghbashlo et al 35 In the work of Aghbashlo et al, 35 an RBF neural network was employed to model biohydrogen production from the photo‐fermentation process with an R 2 of .979, an indication that the predicted biohydrogen produced from the photo‐fermentation process is inconsistent with the observed values. However, the effect of activation function on the RBF performance was not reported.…”

Section: Resultssupporting

confidence: 87%

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

et al. 2021

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This study aimed to investigate the application of radial basis function (RBF) and multilayer perceptron (MLP) artificial neural networks for modeling hydrogen production by co-gasification of rubber and plastic wastes. Both the RBF and MLP neural networks were configured by determining the besthidden neurons that could offer optimized performance. Based on the besthidden neurons, a model architecture of 4-16-1, 4-20-1, 4-17-1, and 4-3-1 was obtained for RBF (with standard activation function), RBF (with ordinary activation function), one-layer MLP, and two-layer MLP, respectively, indicating the number of input nodes, the hidden neurons, and the output nodes. The predicted hydrogen production from the co-gasification closely agrees with the observed values. The 1-layer MLP with R 2 of .990 displayed the best performance with all the input parameters having a significant influence on 9 the model output. The neural network algorithm obtained in this study could be implemented in the eventuality of making a vital decision in the process operation of the co-gasification process for hydrogen production.

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Section: Resultssupporting

confidence: 87%

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

et al. 2021

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“…Goyal et al [21] Daily Evaporation ANN, LS-SVR, FIS, ANFIS Ay and Kisi [22] COD Concentration MLR, MLP, RBF, GRNN, ANFIS, k-MLP He et al [23] River Flow ANN, ANFIS, SVM Asadi et al [24] NOx Concentration ANN, NF Tayfur et al [25] Hydraulic Conductivity SFL, MFL, LM-ANN, NF Piotrowski et al [26] Water Temperature MLP, ANFIS, WNN, KNN Olyaie et al [27] Suspended Sediment Load ANNs, ANFIS, WNN, SRC Estalaki et al [28] Water Quality ER, FSC, SWMM, MUSIC Aghbashlo et al [29] Photo-Biohydrogen Production RBF, FCR Nadiri et al [30] Strength of Geopolymers SFL, MFL, LFL Bagheri et al [31] Landfill Leachate Penetration FIS, ANN Bressane et al [32] Arboreal Recognition FIS, C5, CCNN, KNN, PNN, MLP, RF, DT, SGB, SVM Nabavi-Pelesaraei et al [33] Energy Output ANN, ANFIS Dou and Yang [34] Daily Evapotranspiration ELM, ANFIS, ANN, SVM Choubin et al [35] Suspended Sediment Load CART, ANFIS, MLP, SVM Nadiri et al [36] Effluent Water Parameters FIS, SCFL Raei et al [37] Urban Stormwater MLP, NSGA-II, Fuzzy α-cut, DSS Adnan et al [38] Daily Streamflow ANFIS-PSO, MARS, M5, OP-ELM Kaab et al [39] Environmental Impacts ANN, ANFIS Roy et al [40] Reference Evapotranspiration FA-ANFIS, Ensembles Ly et al [41] Water Quality Modeling LR, DL-ANN, ANFIS Manzar et al [42] Water Quality Index GRNN, Elm-NN, FFNN, SVM, LR, NF Kılıç and Topuz [43] PTE in Volcanic Ash Soils ANN, FLRA Recognizing the inherent uncertainties in climatic conditions, Goyal et al [21] aimed to address the challenges associated with the accurate modeling of daily evaporation predictions in subtropical climates. The methods under comparison included ANN, least squares support vector regression (LS-SVR), FIS, and adaptive neuro-fuzzy inference systems (ANFISs).…”

Section: Studies Environmental Parameters ML Methodsmentioning

confidence: 99%

“…Aghbashlo et al [29] developed a RBF model interfaced with the proposed hybrid fuzzy clustering-ranking (FCR) algorithm to simultaneously maximize the rational and process-energetic efficiencies and minimize the normalized exergy destruction. In order to evaluate the capability of the proposed approach, the conventional fuzzy optimization algorithm was also applied.…”

Section: Studies Environmental Parameters ML Methodsmentioning

confidence: 99%

Fuzzy Machine Learning Applications in Environmental Engineering: Does the Ability to Deal with Uncertainty Really Matter?

Bressane,

Garcia,

Castro

et al. 2024

Sustainability

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Statement of Problem: Environmental engineering confronts complex challenges characterized by significant uncertainties. Traditional modeling methods often fail to effectively address these uncertainties. As a promising direction, this study explores fuzzy machine learning (ML) as an underutilized alternative. Research Question: Although the potential of fuzzy logic is widely acknowledged, can its capabilities truly enhance environmental engineering applications? Purpose: This research aims to deepen the understanding of the role and significance of fuzzy logic in managing uncertainty within environmental engineering applications. The objective is to contribute to both theoretical insights and practical implementations in this domain. Method: This research performs a systematic review carried out in alignment with PRISMA guidelines, encompassing 27 earlier studies that compare fuzzy ML with other methods across a variety of applications within the field of environmental engineering. Results: The findings demonstrate how fuzzy-based models consistently outperform traditional methods in scenarios marked by uncertainty. The originality of this research lies in its systematic comparison and the identification of fuzzy logic’s transparent, interpretable nature as particularly suited for environmental engineering challenges. This approach provides a new perspective on integrating fuzzy logic into environmental engineering, emphasizing its capability to offer more adaptable and resilient solutions. Conclusions: The analysis reveals that fuzzy-based models significantly excel in managing uncertainty compared to other methods. However, the study advocates for a case-by-case evaluation rather than a blanket replacement of traditional methods with fuzzy models. This approach encourages optimal selection based on specific project needs. Practical Implications: Our findings offer actionable insights for researchers and engineers, highlighting the transparent and interpretable nature of fuzzy models, along with their superior ability to handle uncertainties. Such attributes position fuzzy logic as a promising alternative in environmental engineering applications. Moreover, policymakers can leverage the reliability of fuzzy logic in developing ML-aided sustainable policies, thereby enhancing decision-making processes in environmental management.

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“…Two exogenous input parameters were used with an RBF neural network to establish a correlation between the exergetic outputs. A combination fuzzy clustering-ranking algorithm was devised and linked with the RBF model to improve both rational and process exergy efficiency while reducing normalized exergy destruction (Aghbashlo et al, 2016).…”

Section: Applications Of Machine Learning To Optimize Biohydrogen Pro...mentioning

confidence: 99%

Machine learning in biohydrogen production: a review

Alagumalai¹,

Balaji

Shu

et al. 2023

Biofuel Res. J.

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Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high energy yield to replace conventional fossil fuels. However, biohydrogen commercial uptake is mainly hindered by the supply side. As a result, various operating parameters must be optimized to realize biohydrogen commercial uptake on a large-scale. Recently, machine learning algorithms have demonstrated the ability to handle large amounts of data while requiring less in-depth knowledge of the system and being capable of adapting to evolving circumstances. This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production. The accuracy and potential of different machine learning algorithms are reported. Also, the practical implications of machine learning models to realize biohydrogen uptake by the transportation sector are discussed. The review indicates that machine learning algorithms can successfully model non-linear and complex interactions between operational and performance parameters in biohydrogen production. Additionally, machine learning algorithms can help researchers identify the most efficient methods for producing biohydrogen, leading to a more sustainable and cost-effective energy source.

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Multi-objective exergetic optimization of continuous photo-biohydrogen production process using a novel hybrid fuzzy clustering-ranking approach coupled with Radial Basis Function (RBF) neural network

Cited by 26 publications

References 24 publications

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

Fuzzy Machine Learning Applications in Environmental Engineering: Does the Ability to Deal with Uncertainty Really Matter?

Machine learning in biohydrogen production: a review

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