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1. Comparative Environmental and Economic Analysis of Biomethane Production from Co-Digestion of Rice Straw and Spent Mushroom Compost: Application of Zero-Valent Iron Nanoparticles

Author

Ardabili Sina, Pourdarbani Razieh, Maleki Lotfollah, Jafari Gholamhossein, and Hernandez-Hernandez José Luis

Subjects
biogas, sustainable production, waste management, alkaline pretreatment, nanoparticles, digestion, Agriculture (General), S1-972
Abstract

Spent mushroom compost is one of the main potentials for biogas production. In recent years, several studies employed adding nanoparticles and alkaline pretreatment for improving biogas production. The present study is one of the pioneer studies that employ hybrid alkaline pretreatment (0, 5, and 15 mg of NaOH) and zero-valent iron nanoparticles (0, 10, 20, 30, and 40 mg) for improving the co-digestion of spent mushroom compost and rice straw. According to the results, retention time (RT) and nanoparticle (NP) concentrations have the most significant impact on biomethane production (significant at 1% probability level), while the NaOH concentration has the lowest impact on biomethane production (significant at 5% probability level) in comparison with RT and NP concentration. Also, the maximum biomethane production is related to NP40Na15 (about 200% higher than the control). The minimum cumulative biomethane production is related to NP0Na15 (about 30% lower than the control). The lowest relative environmental midpoint impact is related to NP40Na15, which was on average about 60% lower than the control. Adding NPs at high concentrations of NaOH reduces the midpoint impacts. The results of the study could lead to new, ecologically friendly biomethane production methods that make better use of agricultural and organic wastes.

Published
2024
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2. Investigating the Essential Factors for Thresher Losses and Waste in Peanut Harvester Considering Several Varieties of Crop

Author

Tarighi Javad, Azad Mousa, Ardabili Sina, and Mosavi Amir

Subjects
combine harvester, sustainable production, sustainable development, sustainable food production, smart agriculture, food security, Agriculture (General), S1-972
Abstract

This research investigated the performance of a model traction combine harvester (TCH) for harvesting several varieties of peanuts. The effect of independent factors including tested varieties at three levels (local Moghan, Goli Astana, and Turkish), tractor forward speed (FS) at three levels (4, 5, and 6 km·h−1), and rotational speed (RS) of the thresher at three levels (500, 600, 700 rpm) on losses and wastes were studied and investigated. To analyse the obtained data, a factorial experimental design was used in the form of randomised complete blocks, and Duncan‘s multi-range test was used to compare the mean values. According to the findings, the effects of tractor FS and rotary speed of the thresher on the percentage of threshing losses were significant at the probability level of 1%. Also, the interaction effects of variety type and tractor FS, tractor FS, and rotary speed of the thresher on the percentage of threshing losses were significant at the 1% probability level. Also, the effects of variety type, tractor FS, rotary speed of the thresher, and all mutual effects on the percentage of threshing waste were significant at the 1% probability level. The results of comparing the average effects of the main factors showed that the FS of 6 km·h−1 had the highest average threshing loss (82.89%). Threshing losses were obtained in the Turkish variety with the lowest average value (2.51%) and in the local variety with the highest average value (3.53%).

Published
2024
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3. Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature

Author

Zhang, Guodao, Band, Shahab S., Ardabili, Sina, Chau, Kwok-Wing, and Mosavi, Amir

Subjects
Computer Science - Machine Learning, 68T07
Abstract

In this research, dew point temperature (DPT) is simulated using the data-driven approach. Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized as a data-driven technique to forecast this parameter at Tabriz in East Azerbaijan. Various input patterns, namely T min, T max, and T mean, are utilized for training the architecture whilst DPT is the model's output. The findings indicate that, in general, ANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the data-driven technique (machine learning method) and the observed data. Using this prediction toolkit, DPT can be adequately forecasted solely based on the temperature distribution of Tabriz. This kind of modeling is extremely promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales. Whilst the ANFIS model is extremely stable for almost all numbers of membership functions, the BNN model is highly sensitive to this scale factor to predict DPT., Comment: 18 pages, 15 figures

Published
2022
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5. Sustainable Biomethane Production from Sewage Sludge and Wheat Straw Co-Digestion in the Presence of Polypyrrole Fe3O4 Nanoparticles and Alkaline Pretreatment: Life Cycle Assessment Point of View

Author

Jafari Gholamhossein, Ardabili Sina, Pourdarbani Razieh, Abbaszadeh Behzad, and Hernandez-Hernandez Mario

Subjects
anaerobic digestion, bioenergy, biogas, sustainable production, optimization, Agriculture (General), S1-972
Abstract

Biogas production from sewage sludge can be valued in different scenarios of energy usage such as the electricity, heating, and transportation system. Improving the energy production value is the main reason for using different ways such as alkaline pretreatment and nanoparticles (NPs). This investigation uses life cycle assessment (LCA) for evaluating the biomethane production from co-digestion of sewage sludge and wheat straw in the presence of polypyrrole Fe3O4 nanoparticles (0, 20, 40, 60, and 80 mg) and NaOH alkaline pretreatment (0 and 5 mg) at mesophilic anaerobic digestion along with optimization for sustainable biomethane production. Results indicated that retention time (RT) and NaOH concentration had a significant effect on biomethane production. But, the effect of NP concentration on biomethane production was not significant. Also, the maximum biomethane production was obtained by NP40Na0 (about 50% higher than the control). On average, adding the NaOH and NPs improved the biomethane yield by about 25–40% vs in comparison with the control. The maximum biogas production yield was related to NP40Na0 with an enhancement of about 35% in comparison with the control. On average, adding nanoparticles and NaOH alkaline pretreatment improved the biogas production by about 10–20% in comparison with the control. Sustainable biomethane production can be obtained at an NP concentration of 50.24 mg and NaOH concentration of 24.04 mg, which increased the biomethane production by about 198%, and reduced human health, ecosystem quality, climate change, resources, and economic cost by about 87, 86, 92, 95, and 87%, respectively, in comparison with the control.

Published
2023
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6. Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

Author

Nosratabadi, Saeed, Ardabili, Sina, Lakner, Zoltan, Mako, Csaba, and Mosavi, Amir

Subjects
Economics - General Economics
Abstract

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

Published
2021

7. Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

Author

Ardabili, Sina, Mosavi, Amir, Mahmoudi, Asghar, Gundoshmian, Tarahom Mesri, Nosratabadi, Saeed, and Varkonyi-Koczy, Annamaria R.

Subjects
Economics - General Economics
Abstract

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system.

Published
2020
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8. State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Author

Nosratabadi, Saeed, Mosavi, Amir, Keivani, Ramin, Ardabili, Sina, and Aram, Farshid

Subjects
Economics - General Economics
Abstract

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

Published
2020
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9. Hybrid Machine Learning Models for Crop Yield Prediction

Author

Nosratabadi, Saeed, Imre, Felde, Szell, Karoly, Ardabili, Sina, Beszedes, Bertalan, and Mosavi, Amir

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning, Statistics - Machine Learning, 68T05
Abstract

Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of the artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction are evaluated. According to the results, ANN-GWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security., Comment: 5 pages, 2 figures, 3 tables

Published
2020

10. Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

Author

Ardabili, Sina, Beszedes, Bertalan, Nadai, Laszlo, Szell, Karoly, Mosavi, Amir, and Imre, Felde

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, 68T05
Abstract

Hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models., Comment: 6 pages, 6 figures

Published
2020

11. Performance Analysis of Combine Harvester using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

Author

Nadai, Laszlo, Imre, Felde, Ardabili, Sina, Gundoshmian, Tarahom Mesri, Gergo, Pinter, and Mosavi, Amir

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Statistics - Machine Learning, 68T05
Abstract

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy., Comment: 6 pages, 6 figures

Published
2020

12. Life Cycle Assessment of a Hybrid Self-Power Diesel Engine

Author

Hashemi Fatemeh, Pourdarbani Razieh, Ardabili Sina, and Hernandez-Hernandez José Luis

Subjects
hybrid vehicles, diesel engine, life cycle assessment, hybrid self-power generation, optimization, Agriculture (General), S1-972
Abstract

In the present study, an attempt was made to simulate the hybrid self-power generation process from a diesel engine and propose sustainable power-share management for this self-power hybrid electrical-diesel platform from the viewpoints of environmental and economic aspects (life cycle perspective). This concept means that when the engine works at part-load condition, we need to bring the engine operating conditions to optimal load, and along with doing the necessary work, in parallel, the rest of the power be used to generate electricity to be stored in the battery for more consumption in addition to reducing pollution and reducing fuel consumption by moving towards sustainable energy production. This case study examines the differences between using a diesel engine at idle and maximum load conditions when less engine power is required and if using a diesel engine in a diesel-electricity hybrid cycle. According to the obtained results, it can be concluded that to achieve the most negligible environmental impact, a lower share of generating power can be allocated to electricity generation in higher cycles than the share of generating electrical power in lower cycles. In other words, by increasing the engine speed, the amount of power consumption used to generate electricity relative to the power required from the diesel engine should be reduced so that the system suffers the most negligible environmental impact.

Published
2023
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13. Exergo-Environmental Optimization of a Diesel Engine

Author

Pourdarbani Razieh, Ardabili Sina, Akbarpouran Ebrahim, and Hernandez-Hernandez José Luis

Subjects
diesel engine, diesel fuel, exergy, energy, life cycle assessment, Agriculture (General), S1-972
Abstract

Currently, more than half of the road transport fleet uses diesel engines, which are often identified as the primary source of air pollution. This parameter is enough to optimize engine performance and emissions. The engine optimization can be done using several methods, the most notably by modifying the engine structure, changing the type of fuel using additives and biofuels, or achieving the optimal operating range of the engine. Modifying the engine structure and addition of different kinds of materials to optimize fuel is not recommended either due to necessity of vast time input, financial resources, or extensive research. However, the third way to achieve optimal engine performance conditions can be the most accessible option. According to the results, the best operational load for diesel engine is approx. 94–95% of the full load from the multi-objective optimization point of view, indicating that the optimum load can be achieved before the full load condition. At this point, the operator can achieve the brake power of 198.45 kW and brake thermal efficiency of 40.7% in the presence of brake specific fuel consumption of 0.226 kg·kWh−1. At this condition, CO2 emission is 124.85 g·kWh−1, NOx emission 7.34 g·kWh−1, CO emission 0.6 g·kWh−1, unburnt hydrocarbon emission approx. 0.009 g·kWh−1, and soot formation approx. 0.006 g·kWh−1. This point is equal to the exergy efficiency of approx. 35% and the exergy destruction of approx. 45%. In terms of endpoint results, this condition achieved the impact indices of 7.96E-007 in terms of human health, 0.105 PDF·m2·yr. in terms of ecosystem quality, 0.24 kg CO2 eq. in terms of climate change, and 12.96 MJ in terms of resources.

Published
2022
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17. Sustainable Biomethane Production from Sewage Sludge and Wheat Straw Co-Digestion in the Presence of Polypyrrole Fe3O4 Nanoparticles and Alkaline Pretreatment: Life Cycle Assessment Point of View.

Author

Jafari, Gholamhossein, Ardabili, Sina, Pourdarbani, Razieh, Abbaszadeh, Behzad, and Hernandez-Hernandez, Mario

Subjects
SEWAGE sludge digestion, SUSTAINABILITY, SEWAGE sludge, PRODUCT life cycle assessment, WHEAT straw, BIOGAS production
Abstract

Biogas production from sewage sludge can be valued in different scenarios of energy usage such as the electricity, heating, and transportation system. Improving the energy production value is the main reason for using different ways such as alkaline pretreatment and nanoparticles (NPs). This investigation uses life cycle assessment (LCA) for evaluating the biomethane production from co-digestion of sewage sludge and wheat straw in the presence of polypyrrole Fe3O4 nanoparticles (0, 20, 40, 60, and 80 mg) and NaOH alkaline pretreatment (0 and 5 mg) at mesophilic anaerobic digestion along with optimization for sustainable biomethane production. Results indicated that retention time (RT) and NaOH concentration had a significant effect on biomethane production. But, the effect of NP concentration on biomethane production was not significant. Also, the maximum biomethane production was obtained by NP40Na0 (about 50% higher than the control). On average, adding the NaOH and NPs improved the biomethane yield by about 25–40% vs in comparison with the control. The maximum biogas production yield was related to NP40Na0 with an enhancement of about 35% in comparison with the control. On average, adding nanoparticles and NaOH alkaline pretreatment improved the biogas production by about 10–20% in comparison with the control. Sustainable biomethane production can be obtained at an NP concentration of 50.24 mg and NaOH concentration of 24.04 mg, which increased the biomethane production by about 198%, and reduced human health, ecosystem quality, climate change, resources, and economic cost by about 87, 86, 92, 95, and 87%, respectively, in comparison with the control. [ABSTRACT FROM AUTHOR]

Published
2023
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18. The Effect of Biodiesel, Ethanol, and Water on the Performance and Emissions of a Dual-Fuel Diesel Engine with Natural Gas: Sustainable Energy Production through a Life Cycle Assessment Approach

Author

Hashemi-Nejhad, Amir, Najafi, Bahman, Ardabili, Sina, Jafari, Gholamhossein, and Mosavi, Amirhosein

Abstract

Diesel fuel (DF) is a significant power supply in agricultural, industrial, and transportation applications. Establishing sustainable and renewable fuel substitutes for diesel has become increasingly common due to the rising expense of petroleum resources and the pollution rate crises. A biodiesel-DF mixture in a dual-fuel (DuF) diesel engine (DE) can bring favorable environmental results. In the present study, three rates of ethanol (0, 2, and 4%), two rates of biodiesel (0 and 5%), and four rates of water (0, 0.3, 0.6, and 0.9%) were blended with DF. All these samples were considered pilot fuel (PF) in the DuF combustion process with an 80% natural gas (NG) replacement percentage. The combustion process was investigated from engine emissions and performance, power cost, and life cycle assessment (LCA) to obtain a sustainable fuel formulation. As a result, water, ethanol, and the combination of water-ethanol and NG can enhance the DE’s performance by rising the inside pressure of the cylinder. The presence of oxygen content in ethanol can improve the combustion process by pushing the combustion towards complete combustion. The optimum engine performance point at full load was obtained with a fuel sample containing 1.57% biodiesel, 4.38% ethanol, 1.1% water, and 80% NG. In optimum condition, the brake power (BP) was 24.16 kW, and the brake-specific fuel consumption (BSFC) was 60.64 g/kWh. This fuel sample produces 0.46, 364.08, 1.66, and 1088.29 g/kWh of BSCO, BSCO2, BSNOx, and BSO2, respectively. At this point, the energy production cost was $0.783/kWh. The environmental impacts of the combustion process at optimal fuel formulation were 0.34249, 1.00E+02, 1.53E+00, and 1.94E−06, respectively, for ecosystem quality (EQ) (PDF∗m2∗yr), resources (R) (MJ primary), climate change (CCh) (kg CO2 eq), and human health (HH) (DALY). Accordingly, the best fuel combination was selected to be NG+B1.5E4.3W1.1.

Published
2023
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21. A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis

Author

Band, Shahab S., primary, Ardabili, Sina, additional, Yarahmadi, Atefeh, additional, Pahlevanzadeh, Bahareh, additional, Kiani, Adiqa Kausar, additional, Beheshti, Amin, additional, Alinejad-Rokny, Hamid, additional, Dehzangi, Iman, additional, Chang, Arthur, additional, Mosavi, Amir, additional, and Moslehpour, Massoud, additional

Published
2022
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22. Systematic Review of Deep Learning and Machine Learning for Building Energy

Author

Ardabili, Sina, Abdolalizadeh, Leila, Mako, Csaba, Torok, Bernat, Mosavi, Amir, Ardabili, Sina, Abdolalizadeh, Leila, Mako, Csaba, Torok, Bernat, and Mosavi, Amir

Abstract

The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness, and LR-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANNbased techniques provided a medium robustness score.

Published
2022

28. Optimization of performance and emission of compression ignition engine fueled with propylene glycol and biodiesel–diesel blends using artificialintelligence method of ANN-GA-RSM

Author

Karimmaslak, Haleh, Najafi, Bahman, Band, Shahab S., Ardabili, Sina, Haghighat-Shoar, Farid, Mosavi, Amir, Karimmaslak, Haleh, Najafi, Bahman, Band, Shahab S., Ardabili, Sina, Haghighat-Shoar, Farid, and Mosavi, Amir

Abstract

The present study proposes the hybrid machine learning algorithm of artificial neural network-genetic algorithm-response surface methodology (ANN-GA-RSM) to modelthe performance and the emissionsof a single cylinder diesel engine fueled by diesel and propylene glycol additive. The evaluations areperformed using the correlation coefficient (CC), and the root mean square error (RMSE) values. The best model for prediction of the dependent variables is reported ANN-GA with the RMSE values of 0.0398, 0.0368, 0.0529, 0.0354, 0.0509 and 0.0409 and CC 0.988, 0.987, 0.977, 0.994, 0.984, 0.990, respectively for brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), CO, CO2, NOx and SO2. The proposed hybrid model reduces BSFC, NOx, and CO by −30.82%, 21.32%, and 11.32%, respectively. The model also increases the engine efficiency and CO2 emission by 17.29% and 31.05%, respectively, compared to a single RSM in the optimized level of independent variables (69% of biodiesel's oxygen content and 32% of the oxygen content of propylene glycol).

Published
2021

30. Deep learning and machine learning models in biofuels research: Systematic review

Author

ardabili, Sina, Mosavi, Amir, and Várkonyi Kóczy, A. R.

Subjects
other
Abstract

The importance of energy systems and its role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers, but is also important for oil-rich countries. In addition to the nature of these fuels which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating and liquid fuels is very important. Accordingly, the need for handling, modelling, decision making and future forecasting for biofuels can be one of the main challenges for scientists. Recently, machine learning and deep learning techniques have been popular in modeling, optimizing and handling the biodiesel production, consumption and its environmental impacts. The main aim of this study is to evaluate the ML and DL techniques developed for handling biofuels production, consumption and environmental impacts, both for modeling and optimization purposes. This will help for sustainable biofuel production for the future generations. &nbsp

Published
2020
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32. COVID-19 Outbreak Prediction with Machine Learning

Author

Ardabili, Sina F., Mosavi, Amir, Ghamisi, Pedram, Ferdinand, Filip, Varkonyi-Koczy, Annamaria R., Reuter, Uwe, Rabczuk, Timon, Atkinson, Peter, Ardabili, Sina F., Mosavi, Amir, Ghamisi, Pedram, Ferdinand, Filip, Varkonyi-Koczy, Annamaria R., Reuter, Uwe, Rabczuk, Timon, and Atkinson, Peter

Abstract

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.

Published
2020

38. COVID-19 Outbreak Prediction with Machine Learning

Author

Ardabili, Sina, primary, Mosavi, Amir, additional, Ghamisi, Pedram, additional, Ferdinand, Filip, additional, Varkonyi-Koczy, Annamaria, additional, Reuter, Uwe, additional, Rabczuk, Timon, additional, and Atkinson, Peter, additional

Published
2020
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40. Development of a Modified Kinetic Method for Modelling of Biogas Produced from Biomass

Author

Najafi, Bahman, Ardabili, Sina Faizollahzadeh, Shamshirband, Shahab, and Chau, Kwok-wing

Subjects
energy_fuel_technology
Abstract

The kinetics of biogas production from biomass depends on several factors such as: carbon to nitrogen ratio (C/N), reactor temperature (T), and retention time (RT). The purpose of this study was to obtain a new model for predicting biogas production. Spent Mushroom Compost (SMC) was used to produce biogas in a batch type reactor. The experiments were carried out with different C/N ratios (12.1, 20, 30 and 40) and in both mesophilic (35°C) and thermophilic (55°C) temperatures. The results showed that the Maximum biogas production at 35°C, C/N=20 was equal to 41.9 mL/gVS and 55°C, C/N=30 was equal to 51.6 mL/gVS. By using experimental data, a new kinetic model was proposed to predict biogas production. Comparing the values of the results indicate that the total values of RMSE for Logistics, Gampartz and new kinetic models was 0.1906, 0.1830 and 0.1617, respectively. Therefore, the process of anaerobic digestion of biomass can be assumed to be just a chemical reaction, and the new kinetic model is an appropriate alternative to microbial growth models.

Published
2019

41. Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Author

Ardabili, Sina, Mosavi, Amir, and Várkonyi-Kóczy, Annamária R.

Subjects
artificial_intelligence_robotics
Abstract

The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.

Published
2019

42. Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology

Author

Gundoshmian, Tarahom Mesri, Ardabili, Sina, Mosavi, Amir, and Várkonyi-Kóczy, Annamária R.

Subjects
artificial_intelligence_robotics
Abstract

Automated controlling the harvesting systems can significantly increase the efficiency of the agricultural practices and prevent food wastes. Modeling and improvement of the combine harvester can increase the overall performance. Machine learning methods provide the opportunity of advanced modeling for accurate prediction of the highest performance of the machine. In this study, the modeling of combine harvesting id performed using radial basis function (RBF) and the hybrid machine learning method of adaptive neuro-fuzzy inference system (ANFIS) to predict various variables of the combine harvester for the optimal performance. Response surface methodology (RSM) is also used to optimize the models. The comparative study shows that the ANFIS method outperforms the RBF method.

Published
2019

43. Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research

Author

Ardabili, Sina Faizollahzadeh, Mosavi, Amir, and Várkonyi-Kóczy, Annamária R.

Subjects
artificial_intelligence_robotics
Abstract

Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

Published
2019

44. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Author

Mosavi, Amir, Salimi, Mohsen, Faizollahzadeh Ardabili, Sina, Rabczuk, Timon, Shamshirband, Shahaboddin, Varkonyi-Koczy, Annamaria, Mosavi, Amir, Salimi, Mohsen, Faizollahzadeh Ardabili, Sina, Rabczuk, Timon, Shamshirband, Shahaboddin, and Varkonyi-Koczy, Annamaria

Abstract

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

Published
2019

45. Review of soft computing models in design and control of rotating electrical machines

Author

Dineva, Adrienn, Mosavi, Amir, Ardabili, Sina Faizollahzadeh, Vajda, Istvan, Shamshirband, Shahaboddin, Rabczuk, Timon, Chau, Kwok-Wing, Dineva, Adrienn, Mosavi, Amir, Ardabili, Sina Faizollahzadeh, Vajda, Istvan, Shamshirband, Shahaboddin, Rabczuk, Timon, and Chau, Kwok-Wing

Abstract

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines.

Published
2019

50. An intelligent artificial neural network-response surface methodology method for accessing the optimum biodiesel and diesel fuel blending conditions in a diesel engine from the viewpoint of exergy and energy analysis

Author

Najafi, Bahman, Faizollahzadeh Ardabili, Sina, Mosavi, Amir, Shamshirband, Shahaboddin, Rabczuk, Timon, Najafi, Bahman, Faizollahzadeh Ardabili, Sina, Mosavi, Amir, Shamshirband, Shahaboddin, and Rabczuk, Timon

Abstract

Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25–30% of full load, which is the same as the calculated range obtained from mathematical modeling.

Published
2018

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