Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production

Najafi, Bahman; Ardabili, Sina Faizollahzadeh; Shamshirband, Shahaboddin; Chau, Kwok Wing; Rabczuk, Timon

doi:10.1080/19942060.2018.1502688

Cited by 77 publications

(47 citation statements)

References 41 publications

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“…The artificial neural network as an AI-based model is a mathematical model aiming to handle non-linear relationship of input-output data set [49]. ANN has proved to be effective with regards to complex function in various fields, including prediction, pattern recognition, classification, forecasting, control system and simulation [50,51].…”

Section: Feed Forward Neural Network (Ffnn) Conceptmentioning

confidence: 99%

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

et al. 2019

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The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three different AI models (feed forward neural network-FFNN, adaptive neural fuzzy inference system-ANFIS and least square support vector machine-LSSVM) for the seven stations located in the Turkish Republic of Northern Cyprus (TRNC). Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s precipitation through its own data at previous time steps while in scenario 2, the central station’s data were imposed into the models, in addition to each station’s data, as exogenous input. Afterwards, the ensemble modeling was generated to improve the performance of the precipitation predictions. To end this aim, two linear and one non-linear ensemble techniques were used and then the obtained outcomes were compared. In terms of efficiency measures, the averaging methods employing scenario 2 and non-linear ensemble method revealed higher prediction efficiency. Also, in terms of Skill score, non-linear neural ensemble method could enhance predicting efficiency up to 44% in the verification step.

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Section: Feed Forward Neural Network (Ffnn) Conceptmentioning

confidence: 99%

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

et al. 2019

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“…Accordingly, 1 kg of methane fuel releases 2. 75 kg of carbon dioxide (Molar mass of CO 2 per Molar mass of CH 4 = 44 ÷ 16 = 2.75) (Faizollahzadeh Ardabili, Najafi, Ghaebi, Shamshirband, & Mostafaeipour, 2017;Najafi, Faizollahzadeh Ardabili, Shamshirband, Chau, & Rabczuk, 2018) . According to Table 3, the amount of methane produced from the anaerobic digestion of the SMC is 8690.8 × 10 3 m 3 or 6231.3 tons where the density of methane is 0.717 kg/m 3 (8690.8 × 0.717 = 6231.3).…”

Section: Reduction Of Carbon Dioxide Emissionsmentioning

confidence: 99%

Spent mushroom compost (SMC) as a source for biogas production in Iran

Najafi

Ardabili

Shamshirband

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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The first aim of the present study is to investigate the feasibility of the biogas production potential from Spent Mushroom Compost. Hence, its secondary aim is to study the replacement of this energy with the fossil energy source and its impact on the reduction of greenhouse gases emission. The production potential of biogas was evaluated in a laboratory-scale Open Outlet Flow Batch Type Anaerobic digester. Setups of biogas production were set at temperatures of 35°C and 55°C. Results indicate that the cumulative production of biogas at 55°C was more than that at 35°C (364.8 and 314.11 L/kg.VS, respectively). The energy required for 1 ton of spent mushroom compost in one period of production was 204.24-5.35T kW.h/ton. The amount of energy produced in a period of biogas production was calculated to be 277.95 kW.h/ton at 35°C and 322.84 kW.h/ton at 55°C. Results show that the total produced spent mushroom compost of the country is equal to 414,491.7 tons per year. Accordingly, the potential of methane production was calculated to be 8690.8 × 103 m 3 per year. It can also reduce 0.02% of the total annual production of CO 2 in Iran. ARTICLE HISTORY

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“…MLP [16] is a simple neural network composed of fully connected layers [23]. This baseline is a three-layer neural network with 16 neurons per layer.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

et al. 2019

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Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

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Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production

Cited by 77 publications

References 41 publications

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

Spent mushroom compost (SMC) as a source for biogas production in Iran

Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

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