Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process

Salameh, Tareq; Sayed, Enas Taha; Olabi, Abdul Ghani; Hdaib, Ismail I.; Allan, Yazeed; Alkasrawi, Malek; Abdelkareem, Mohammad Ali

doi:10.3390/fermentation8100483

Cited by 14 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the emergence of artificial intelligence (AI) tools, it is now possible to identify and use the patterns in available datasets to predict the outcome for a new input without conducting detailed laboratory studies [215]. Machine learning models (data-driven models), such as artificial neural networks, random forests, support vector machines, multilinear regression, and decision trees, have been successfully applied in microalgae biomass conversions technologies such as pyrolysis [23,216], gasification [217], hydrothermal liquefaction [218] and biological hydrogen production [219] for the prediction and optimization of the bioenergy yield. Despite their success, machine learning-assisted predictions in the bioenergy field are still in the initial stages of development.…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

From Microalgae to Bioenergy: Recent Advances in Biochemical Conversion Processes

et al. 2023

View full text Add to dashboard Cite

Concerns about rising energy demand, fossil fuel depletion, and global warming have increased interest in developing and utilizing alternate renewable energy sources. Among the available renewable resources, microalgae biomass, a third-generation feedstock, is promising for energy production due to its rich biochemical composition, metabolic elasticity, and ability to produce numerous bioenergy products, including biomethane, biohydrogen, and bioethanol. However, the true potential of microalgae biomass in the future bioenergy economy is yet to be realized. This review provides a comprehensive overview of various biochemical conversion processes (anaerobic digestion, direct biophotolysis, indirect biophotolysis, photo fermentation, dark fermentation, microalgae-catalyzed photo fermentation, microalgae-catalyzed dark fermentation, and traditional alcoholic fermentation by ethanologenic microorganisms) that could be adapted to transform microalgae biomass into different bioenergy products. Recent advances in biochemical conversion processes are compiled and critically analyzed, and their limitations in terms of process viability, efficacy, scalability, and economic and environmental sustainability are highlighted. Based on the current research stage and technological development, biomethane production from anaerobic digestion and bioethanol production from traditional fermentation are identified as promising methods for the future commercialization of microalgae-based bioenergy. However, significant challenges to these technologies’ commercialization remain, including the high microalgae production costs and low energy recovery efficiency. Future research should focus on reducing microalgae production costs, developing an integrated biorefinery approach, and effectively utilizing artificial intelligence tools for process optimization and scale-up to solve the current challenges and accelerate the development of microalgae-based bioenergy.

show abstract

Section: Challenges and Future Prospectsmentioning

confidence: 99%

From Microalgae to Bioenergy: Recent Advances in Biochemical Conversion Processes

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Although physical and mathematical modeling have made significant progress in simulating various processes, their accuracy is still constrained by the constants and parameters that are often presupposed [28,29]. Artificial intelligence has demonstrated high effectiveness in the accurate modeling and optimization of various processes, such as biodiesel production from palm kernel shell [30], electricity generation in fuel cells [31][32][33], microbial fuel cells [34][35][36], alternative fuels [37], heat transfer and waste heat recovery [38][39][40], biohydrogen production [41,42], etc. As a general rule, system identification and parameter identification applications, optimization is a critical technique [34,43].…”

Section: Introductionmentioning

confidence: 99%

Maximization of CO2 Capture Capacity Using Recent RUNge Kutta Optimizer and Fuzzy Model

et al. 2023

Self Cite

View full text Add to dashboard Cite

This study aims to identify the optimal operating parameters for the carbon dioxide (CO2) capture process using a combination of artificial intelligence and metaheuristics techniques. The main objective of the study is to maximize CO2 capture capacity. The proposed method integrates fuzzy modeling with the RUNge Kutta optimizer (RUN) to analyze the impact of three operational factors: carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio. These factors are considered to maximize the CO2 capture. A fuzzy model was developed based on the measured data points to simulate the CO2 capture process in terms of the stated parameters. The model was then used to identify the optimal values of carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio using RUN. The results of the proposed method are then compared with an optimized performance using the response surface methodology (RSM) and measured data to demonstrate the superiority of the proposed strategy. The results of the study showed that the suggested technique increased the CO2 capture capacity from 6.39 to 6.99 by 10.08% and 9.39%, respectively, compared to the measured and RSM methods. This implies that the proposed method is an effective approach to maximize the CO2 capture capacity. The results of this study can be used to improve the performance of the CO2 capture process in various industrial applications.

show abstract

“…The novelty of this study lies in the combination of an advanced optimization method to improve the solubility of CO 2 in the capture solvents. The combination of the robustness of ANFIS modeling and metaheuristic optimization methods has proven to be highly efficient in finding reliable and feasible outcomes [42,43]. This approach has the potential to significantly improve the efficiency and cost-effectiveness of the CO 2 capture processes.…”

Section: Introductionmentioning

confidence: 99%

Improving CO2 Absorption Using Artificial Intelligence and Modern Optimization for a Sustainable Environment

Nassef

2023

Sustainability

View full text Add to dashboard Cite

One of the essential factors in maintaining environmental sustainability is to reduce the harmful effects of carbon dioxide (CO2) emissions. This can be performed either by reducing the emissions themselves or capturing and storing the emitted CO2. This work studies the solubility of carbon dioxide in the capturing solvent, which plays a crucial role in the effectiveness and cost-efficiency of carbon capture and storage (CCS). Therefore, the study aims to enhance the solubility of CO2 by integrating artificial intelligence (AI) and modern optimization. Accordingly, this study consists of two consecutive stages. In the first stage, an adaptive neuro-fuzzy inference system (ANFIS) model as an AI tool was developed based on experimental data. The mol fraction was targeted as the model’s output in terms of three operating parameters; the concentration of tetrabutylphosphonium methanesulfonate [TBP][MeSO3], temperature, and pressure of CO2. The operating ranges are (2–20 wt%), (30–60 °C), and (2–30 bar), respectively. Based on the statistical measures of the root mean squared error (RMSE) and the predicted R2, the ANFIS model outperforms the traditional analysis of variance (ANOVA) modeling technique, where the resulting values were found to be 0.126 and 0.9758 for the entire samples, respectively. In the second stage, an improved grey wolf optimizer (IGWO) was utilized to determine the optimal operating parameters that increase the solubility of CO2. The optimal values of the three operating parameters that improve the CO2 solubility were found to be 3.0933 wt%, 40.5 °C, and 30 bar, respectively. With these optimal values, the collaboration between the ANFIS and IGWO produced an increase of 13.4% in the mol fraction compared to the experimental data and the response surface methodology. To demonstrate the efficacy of IGWO, the obtained results were compared to the results of four competitive optimization techniques. The comparison showed that the IGWO demonstrates superior performance. Overall, this study provided a cost-efficient approach based on AI and modern optimization to enhance CO2 solubility in CCS.

show abstract

Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process

Cited by 14 publications

References 37 publications

From Microalgae to Bioenergy: Recent Advances in Biochemical Conversion Processes

From Microalgae to Bioenergy: Recent Advances in Biochemical Conversion Processes

Maximization of CO2 Capture Capacity Using Recent RUNge Kutta Optimizer and Fuzzy Model

Improving CO2 Absorption Using Artificial Intelligence and Modern Optimization for a Sustainable Environment

Contact Info

Product

Resources

About