Effects of utilization of solid and semi-solid organic waste using pyrolysis techniques

Alsaqoor, Sameh; Alahmer, Ali; Aljabarin, Nader; Gougazeh, Mosa; Czajczyńska, Dina; Krzyżyńska, Renata

doi:10.1109/irec.2017.7925998

Cited by 5 publications

(3 citation statements)

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“…It was also demonstrated that the CO 2 capture process resulted in very stable carbonate products in the bulk crystals, which necessitate further heat treatment (between 600 and 1400 K) to release captured CO 2 . Ca(OH) 2 and Mg(OH) 2 powders were found to be efficient adsorbents, with the amount of CO 2 adsorbed inversely proportional to the domain size of the specimen [22,26].…”

Section: Introductionmentioning

confidence: 99%

“…Massive fossil fuel consumption has severe environmental consequences, including global warming and accompanying anomalous droughts and floods [1,2]. Various approaches have been explored to prevent global warming, including waste heat recovery [3][4][5][6], the development of renewable energy sources [7,8], and carbon capture and storage [9,10].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

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“…Therefore, governments and researchers are searching seriously for appropriate solutions either to reduce CO 2 emissions or to minimize their undesired effects. To address this problem, several strategies have been suggested to address global warming, including improving the efficacy of existing processes through waste heat recovery [5][6][7] in sectors that consume high levels of energy, such as ceramics, aluminum [8], cement [9,10], steel [11], and so on. Furthermore, efficient and ecologically friendly energy conversion technologies, such as fuel cells powered by renewable energy sources [12][13][14], are being implemented.…”

Section: Introductionmentioning

confidence: 99%

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

Nassef

2023

Sustainability

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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.

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Optimal Water Addition in Emulsion Diesel Fuel Using Machine Learning and Sea-Horse Optimizer to Minimize Exhaust Pollutants from Diesel Engine

Alahmer

Al-Amayreh

et al. 2023

Atmosphere

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Water-in-diesel (W/D) emulsion fuel is a potentially viable diesel fuel that can simultaneously enhance engine performance and reduce exhaust emissions in a current diesel engine without requiring engine modifications or incurring additional costs. In a consistent manner, the current study examines the impact of adding water, in the range of 5–30% wt. (5% increment) and 2% surfactant of polysorbate 20, on the performance in terms of brake torque (BT) and exhaust emissions of a four-cylinder four-stroke diesel engine. The relationship between independent factors, including water addition and engine speed, and dependent factors, including different exhaust released emissions and BT, was initially generated using machine learning support vector regression (SVR). Subsequently, a robust and modern optimization of the sea-horse optimizer (SHO) was run through the SVR model to find the optimal water addition and engine speed for improving the BT and lowering exhaust emissions. Furthermore, the SVR model was compared to the artificial neural network (ANN) model in terms of R-squared and mean square error (MSE). According to the experimental results, the BT was boosted by 3.34% compared to pure diesel at 5% water addition. The highest reduction in carbon monoxide (CO) and unburned hydrocarbon (UHC) was 9.57% and 15.63%, respectively, at 15% of water addition compared to diesel fuel. The nitrogen oxides (NOx) emissions from emulsified fuel were significantly lower than those from pure diesel, with a maximum decrease of 67.14% at 30% water addition. The suggested SVR-SHO model demonstrated superior prediction reliability, with a significant R-Squared of more than 0.98 and a low MSE of less than 0.003. The SHO revealed that adding 15% water to the W/D emulsion fuel at an engine speed of 1848 rpm yielded the optimum BT, CO, UHC, and NOx values of 49.5 N.m, 0.5%, 57 ppm, and 369 ppm, respectively. Finally, these outcomes have important implications for the potential of the SVR-SHO approach to minimize engine exhaust emissions while maximizing engine performance.

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Effects of utilization of solid and semi-solid organic waste using pyrolysis techniques

Cited by 5 publications

References 4 publications

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

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

Optimal Water Addition in Emulsion Diesel Fuel Using Machine Learning and Sea-Horse Optimizer to Minimize Exhaust Pollutants from Diesel Engine

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