Optimizing biodiesel production from abundant waste oils through empirical method and grey wolf optimizer

Samuel, Olusegun David; Okwu, Modestus O.; Oyejide, Oluwayomi J.; Taghinezhad, Ebrahim; Afzal, Asif; Kaveh, Mohammad

doi:10.1016/j.fuel.2020.118701

Cited by 106 publications

(50 citation statements)

References 96 publications

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“…The precision of both ANN and ANFIS models were investigated with the aid of Eqs. 2-6 as applied by Samuel et al (2020b).…”

Section: Assessment Of the Development Modelsmentioning

confidence: 99%

“…The lower calorific value (LCV) of TSOME (42.53 MJ/kg) was lower than that of B0 (43.78 MJ/kg). HSOME possessing LCV can lead to an upsurge in the brake-specific consumption (Xue et al, 2011;Adaileh and AlQdah, 2012;Samuel et al, 2020b).…”

Section: Fuel Properties Of Tsome Producedmentioning

confidence: 99%

“…Refs. (Uzun et al, 2012;Samuel et al, 2020a;2020b) hinted that optimization is a prerequisite to boost yield and biofuel production in a biorefinery plant. Among high-level optimization techniques, a breed of Artificial Neural Network (ANN) and the Fuzzy Logic (FL), which gives the hybrid algorithm, Adaptive Neuro-Fuzzy Inference System (ANFIS), has been the preferred option in terms of problem-solving, the combination has numerical advantages discussed elsewhere (Okwu and Adetunji, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Modelling of Nicotiana Tabacum L. Oil Biodiesel Production: Comparison of ANN and ANFIS

et al. 2021

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Among the modern computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are preferred because of their ability to deal with non-linear modelling and complex stochastic dataset. Nondeterministic models involve some computational complexities while solving real-life problems but would always produce better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling tobacco seed oil methyl ester (TSOME) production from underutilized tobacco seeds in the tropics. The dataset for the models was obtained from an earlier study which focused on design of the experiment on TSOME production. This study is an an exposition of the influence of transesterification parameters such as reaction duration (T), methanol/oil molar ratio (M:O), and catalyst dosage on the TSOME/biodiesel yield. A multi-layer ANN model with ten hidden layers was trained to simulate the methanolysis process. The ANFIS approach was further implemented to model TSOME production. A comparison of the formulated models was completed by statistical criteria such as coefficient of determination (R2), mean average error (MAE), and average absolute deviation (AAD). The R2 of 0.8979, MAE of 4.34468, and AAD of 6.0529 for the ANN model compared to those of the R2 of 0.9786, MAE of 1.5311, and AAD of 1.9124 for the ANFIS model. The ANFIS model appears to be more reliable than the ANN model in predicting TSOME production in the tropics.

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“…The precision of both ANN and ANFIS models were investigated with the aid of Eqs. 2-6 as applied by Samuel et al (2020b).…”

Section: Assessment Of the Development Modelsmentioning

confidence: 99%

Section: Fuel Properties Of Tsome Producedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling of Nicotiana Tabacum L. Oil Biodiesel Production: Comparison of ANN and ANFIS

et al. 2021

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“…Accordingly, we evaluate the effect of the topology of LSTM and CNN models selected by different bio-inspired optimization algorithms on the error reduction and predictive performance of the model. The compared state-of-the-art algorithms include the GA [35], PSO [33], DE [30], GWO [26], ABC [29], WO [31], CS algorithm [28], and BAT algorithm [34]. By comparing the prediction ability of the proposed model to these obtained by the other nine models established using the compared bio-inspired algorithms shown in Table 11, we can realize that across the three datasets, the proposed model achieved the highest precision, recall, accuracy, and F-score, with limited error rates and low SD values.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Therefore, it is useful to employ the swarm intelligence optimization techniques for enabling the networks to automatically tune their hyperparameters besides the layer connections and make the optimal utilization of the redundant computing resources. Grey wolf optimizer (GWO) [26], antlion optimization (ALO) [27], crow search (CS) algorithm [28], artificial bee colony (ABC) [29], differential evolution (DE) algorithm [30], whale optimization (WO) algorithm [31], Salp swarm algorithm [32], PSO [33], bat optimization (BAT) algorithm [34], and genetic algorithm (GA) [35] are some biologically-inspired algorithms investigated in optimization purposes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

et al. 2021

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Ovarian cancer (OC) is a common reason for mortality among women. Deep learning has recently proven better performance in predicting OC stages and subtypes. However, most of the state-of-the-art deep learning models employ single modality data, which may afford low-level performance due to insufficient representation of important OC characteristics. Furthermore, these deep learning models still lack to the optimization of the model construction, which requires high computational cost to train and deploy them. In this work, a hybrid evolutionary deep learning model, using multi-modal data, is proposed. The established multi-modal fusion framework amalgamates gene modality alongside with histopathological image modality. Based on the different states and forms of each modality, we set up deep feature extraction network, respectively. This includes a predictive antlion-optimized long-short-term-memory model to process gene longitudinal data. Another predictive antlion-optimized convolutional neural network model is included to process histopathology images. The topology of each customized feature network is automatically set by the antlion optimization algorithm to make it realize better performance. After that the output from the two improved networks is fused based upon weighted linear aggregation. The deep fused features are finally used to predict OC stage. A number of assessment indicators was used to compare the proposed model to other nine multi-modal fusion models constructed using distinct evolutionary algorithms. This was conducted using a benchmark for OC and two benchmarks for breast and lung cancers. The results reveal that the proposed model is more precise and accurate in diagnosing OC and the other cancers.

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Comparative of various bio‐inspired meta‐heuristic optimization algorithms in performance and emissions of diesel engine fuelled with B5 containing water and cerium oxide additive blends

Khalife

Kaveh²,

Younesi

et al. 2022

Intl J of Energy Research

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Summary In this study, a diesel engine combustion was modeled to estimate engine performance and emissions for the first time in the field of engine combustion. Four different algorithms including Grasshopper Optimization Algorithm, Ant Lion Optimizer, and Gray Wolf Optimization as well as Artificial Neural Network were employed to predict thermal efficiency, fuel consumption, CO, HC, and NOx emissions of a diesel engine fueled with diesel‐biodiesel blend emulsions containing water and cerium oxide nano additives. The models proposed were developed using two inputs (fuel type and engine load). The results showed that the Gray Wolf Optimization led to maximum coefficient correlation (0.9940 and 0.9966) and minimum Mean Square Error compared with the other employed algorithms for brake thermal efficiency and brake‐specific fuel consumption. The best results were obtained for Gray Wolf Optimization, Ant Lion Optimizer, and Grasshopper Optimization Algorithm, respectively. The same sequence was also found for estimating engine emissions. However, Gray Wolf Optimization showed slightly better result for estimation of engine emission than engine performance. In overall, the results of Gray Wolf Optimization were in perfect agreement with the experimental values compared to the other nature‐inspired algorithms as well as Artificial Neural Network in predicting fuel combustion. The model proposed can find application in fuel and engine manufacturers.

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Optimizing biodiesel production from abundant waste oils through empirical method and grey wolf optimizer

Cited by 106 publications

References 96 publications

Modelling of Nicotiana Tabacum L. Oil Biodiesel Production: Comparison of ANN and ANFIS

Modelling of Nicotiana Tabacum L. Oil Biodiesel Production: Comparison of ANN and ANFIS

Multi-Modal Evolutionary Deep Learning Model for Ovarian Cancer Diagnosis

Comparative of various bio‐inspired meta‐heuristic optimization algorithms in performance and emissions of diesel engine fuelled with B5 containing water and cerium oxide additive blends

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