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machine learning optimization of integrating an advanced thermal-electrochemical plant with an oxy-biogas fuel plant employing a CO2 capture process

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ENERGY CONVERSION AND MANAGEMENT 2025年 335卷

作者： Feili, Milad Nourani, Pejman Haghghi, Maghsoud Abdollahi Bahman, Ammar M. Univ Mohaghegh Ardabili Fac Engn Dept Mech Engn POB 179 Ardebil Iran Univ Tehran Coll Engn Sch Mech Engn Tehran Iran Urmia Univ Sch Engn Dept Mech Engn Orumiyeh Iran Elm Ofann Univ Coll Sci & Technol Dept Mech Engn Orumiyeh Iran Kuwait Univ Coll Engn & Petr Mech Engn Dept POB 5969 Safat 13060 Kuwait

This study addresses boosting biogas utilization through oxy-fuel combustion and integrating an innovative multigeneration system. This system allows advanced thermal-electrochemical integration for electric power, cooling, heat, and liquefied hydrogen generation. This approach reduces energy loss and incorporates a CO2 capture unit. Hence, the integrated subsystems include an oxy-biogas combustion power plant, a supercriticalCO2 power plant, an organic Rankine cycle, an NH3-H2O combined coolant and power cycle, a solid oxide electrolysis cell, and a Claude hydrogen cycle. The study presents a complete examination covering thermodynamic, sustainability, and economic perspectives and detailed parametric assessments, showing the combustion temperature as the most influential parameter. Subsequently, an optimization process is conducted, employing a multi-objective strategy utilizing machine learning techniques based on artificial neural networks and multi-objective grey wolf optimization. Considering the tri-objective scenario with the exergy efficiency, net present value, and total unit cost of products as objective functions, their optimal values are calculated at 47.75 %, 17.72 M$, and 28.13 $/GJ, respectively. Under the tri-objective optimization scenario, the total exergy destruction equals 4630 kW, with the combustion chamber as the most important contributor. Also, the sustainability index and payback period are found at 1.92 and 17.7 M$, respectively. Besides, these conditions exhibit liquefied hydrogen output of 2.9 m3/day, costing 3.37 $/GJ. This research highlights that the integration of oxy-biogas fuel combustion with the designed multigeneration system can enhance biogas utilization, achieving improved thermodynamic efficiency and economic performance while supporting the sustainable production of high-value energy products.

关键词： Biogas Oxy-fuel combustion Cascade thermal recovery CO2 capture Hydrogen liquefaction machine learning optimization

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Energy system design based on municipal solid waste gasification and SOFC for multi-demand applications through multi-heat recovery approach: 4E assessment and machine learning optimization

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 195卷

作者： Balali, Adel Moghimi, Mahdi Iran Univ Sci & Technol Sch Mech Engn Tehran Iran

Municipal solid waste gasification, an environmentally friendly method that complies with the legal limits of greenhouse gas emissions, has been used in this research to supply fuel for solid oxide fuel cell (SOFC). Using the multi-heat recovery approach, the proposed system can satisfy various demands, including electricity, cooling load, hydrogen, and freshwater for domestic and commercial sectors. This study addresses critical gaps in the existing literature by demonstrating the effectiveness of using a cascaded ORC with LNG for waste heat recovery and evaluating techno-economic and environmental impacts through a comprehensive parametric and sensitivity analysis. The simulation of the proposed system has been implemented in MATLAB software, and the influence of design parameters on system outputs and performance indicators has been discussed through a parametric and sensitivity analysis. Investigations reveal that the fuel utilization factor is the most influential parameter on system outputs, affecting the total exergy efficiency and cost rate by 44.7 % and 46.7 %, respectively. Also, the gasification unit has the largest contribution to the total exergy destruction of the proposed system, accounting for 39.98 % (379.61 kW) due to incomplete combustion and thermodynamically unfavorable chemical reactions. Finally, a new optimization method based on data science and machine learning has been used by incorporating the artificial neural network with the genetic algorithm. According to the tri-objective optimization results, the system's optimal state exhibits a 12.07% enhancement in exergy efficiency, a 7.35% reduction in the cost rate, and a 3.9% rise in CO2 emission index compared to the design conditions.

关键词： Multi-generation system Gasification SOFC Desalination machine learning optimization

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Steady flow properties and spectral absorption potential of supercritical carbon dioxide nanofluids: Experimental comparison and machine learning optimization

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POWDER TECHNOLOGY 2024年 434卷

作者： Su, Zixiang Yang, Liu Zhao, Ning Song, Jianzhong Li, Xiaoke Wu, Xiaohu Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China Nanjing Forestry Univ Coll Mat Sci & Engn Nanjing 210037 Peoples R China Chengdu Univ Technol Coll Mat & Chem & Chem Engn Chengdu 610059 Peoples R China Shandong Inst Adv Technol Jinan 250100 Peoples R China

To improve the performance of the power cycle and accelerate the realization of the carbon neutrality goal, the development and application of nanofluids are imminent. In this research, we established a supercritical nanofluid spectral absorbance prototype and prepared seven supercritical carbon dioxide nanofluids for various valuable works. The experiments indicated that titanium carbide/supercritical carbon dioxide had the most outstanding stability with the shortest stabilization time (3.2 min) and the smallest fluctuation amplitude in spectral irradiance (+/- 6.33%). Graphene/supercritical carbon dioxide emerged as the most competitive choice in terms of deposition characteristics and spectral absorption capacity, boasting a deposition density and spectral absorbance of 8.146% and 86.39%. Titanium was the most suitable raw material for constructing the prototype. Then, a performance factor was introduced to quantify the contribution of stability and deposition characteristics, and hydroxylated multi-walled carbon nanotube/supercritical carbon dioxide was found to have the most outstanding comprehensive performance with promising applications. Finally, we combined particle swarm optimization and artificial neural networks to develop an improved machine learning optimization model. Compared to the original prediction model, the root mean square error and mean absolute error of this model were reduced by 64.02% and 66.8%. Significantly, the spectral irradiance through the supercritical carbon di- oxide nanofluid was reduced by 68.21% compared to the base working fluid, providing guidelines for improving the performance of the power cycle.

关键词： Supercritical carbon dioxide nanofluid Stability analysis Deposition characterization Spectral absorbance machine learning optimization

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Graphene-Based Metasurface Terahertz Biosensing Platform for Accurate Alpha-Fetoprotein Detection in Liver Cancer Diagnosis Enhanced with machine learning optimization

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PLASMONICS 2025年 1-14页

作者： Wekalao, Jacob Almawgani, Abdulkarem H. M. Ghodhbani, Refka Adam, Yousif S. Gumaih, Hussein S. Ali, Naim Ben Patel, Shobhit K. Univ Sci & Technol China Dept Opt & Opt Engn Hefei 230026 Peoples R China Najran Univ Coll Engn Elect Engn Dept Najran Saudi Arabia Northern Border Univ Ctr Scientifc Res & Entrepreneurship Ar Ar 73213 Saudi Arabia Najran Univ Coll Sci & Arts Biol Dept Najran Saudi Arabia Univ Hail Coll Engn Dept Ind Engn Hail City 81451 Saudi Arabia Marwadi Univ Dept Comp Engn Rajkot 360003 India

This research presents a terahertz sensor for Alpha-fetoprotein (AFP) detection based on copper metasurfaces. The device's structure incorporates multiple metallic layers deposited on silicon dioxide, with a unique combination of components: an E-shaped gold resonator working in tandem with a circular copper element and a gold ring resonator. Simulations conducted via COMSOL Multiphysics demonstrate remarkable sensing capabilities, with the device achieving 1000 GHzRIU-1 sensitivity, a Figure of Merit of 15.152 RIU(-)1, and a Detection Limit of 0.125. The optimized performance was done by examining various parameters including graphene's chemical potential, angles of incident waves, and dimensional aspects of the structure. The sensor exhibited consistent performance within the 0.94-1.02 THz frequency range, maintaining transmittance above 73%. Additionally, a stacking ensemble machine learning approach is leveraged to improve the sensor performance, achieving exceptional accuracy with coefficient of determination values ranging from 97 to 100%.

关键词： Aha-Fetoprotein Terahertz machine learning optimization Cancer Biomarker Sensing Stacking Ensemble Method

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4E analysis and machine learning optimization of a geothermal-based system integrated with ejector refrigeration cycle for efficient hydrogen production and liquefaction

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第82期48卷 31875-31904页

作者： Sangesaraki, Alireza Gholizadeh Gharehghani, Ayat Mehrenjani, Javad Rezazadeh Iran Univ Sci & Technol Sch Mech Engn Tehran Iran

This study presents a new geothermal-based system for power generation and liquid hydrogen production. Depending on the network requirements, part of the generated power is supplied to the network and the remaining part enters the PEM electrolyzer to produce hydrogen. The produced hydrogen is then pre-cooled by the ejector cooling cycle before entering the liquefaction cycle. The ejector refrigeration cycle not only uses geothermal waste heat but also facilitates the liquefaction process through precooling hydrogen;therefore, it significantly improves the overall performance of the integrated system. In addition, the modified liquefaction cycle is suggested, which is superior to common ones. After simulating, some key parameters are identified and a comprehensive parametric research is carried out to investigate the thermodynamic, economic and environmental performance in different conditions. The optimization procedure is implemented through a genetic algorithm approach and the objective functions are defined. The results reveal that the double flash power cycle has the greatest effect on exergy destruction rate and total cost rate. In the optimal case, the total cost rate, the liquefied hydrogen rate, the total exergy efficiency, net power generation, and CO2 reduction rate are determined as 181.71 $/h, 59.92 kg/h, 25.27%, 4.03 MW, and 1421.2 kg/h, respectively. Also, coupling an artificial neural network with genetic algorithm significantly lessens optimization time.& COPY;2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Geothermal energy Multi-generation system Hydrogen production machine learning optimization Collins liquefaction cycle

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Optimal Sizing and Placement of Renewable Energy Systems in Smart Grids using machine learning optimization 12

Optimal Sizing and Placement of Renewable Energy Systems in ...

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12th International Conference on Smart Grid (ICSmartGrid)

作者： Kumar, Polamarasetty P. Nuvvula, Ramakrishna S. S. Shezan, Sk. A. Satyanarayana, Vanam Ahammed, Syed Riyaz SivaSubramanyamReddy, R. Ali, Ahmed GMR Inst Technol Dept Elect & Elect Engn Rajam India NITTE Deemed be Univ NMAM Inst Technol Deparmtent Elect & Elect Engn Mangaluru Karnataka India Engn Inst Technol Melbourne Campus Dept Elect Engn & Ind Automat Melbourne Vic 3000 Australia Vaagdevicoll Engn Dept Elect Engn Warangal Telangana India NITTE Deemed be Univ NMAM Inst Technol Dept Elect & Commun Engn Mangaluru Karnataka India Sri Kalahasteeswara Inst Technol SKIT Dept EEE Srikalahasti Andhra Pradesh India Univ Johannesburg Dept Elect & Elect Engn Technol Johannesburg South Africa

ISBN: (纸本)9798350361612;9798350361629

This study investigates the optimal sizing and placement of renewable energy systems within a smart grid framework using advanced optimization techniques. Through the application of genetic algorithms, we identify the most effective configuration to enhance energy generation while minimizing system costs and environmental impact. Our optimization model converged after 100 iterations, revealing optimal sizing parameters of 500 kW for solar panels and 300 kW for wind turbines, projected to generate approximately 900 MWh/year and 600 MWh/year, respectively. Moreover, our optimized placement strategy entails installing solar panels on commercial building rooftops and wind turbines on the urban outskirts. To validate our approach, energy generation was forecasted for the next 12 months using the optimized parameters, showing close alignment with historical patterns. These findings highlight the efficacy of our optimization model in capturing system dynamics and accurately predicting future energy production. Overall, our study underscores the importance of integrating advanced optimization methodologies with renewable energy planning to foster sustainable energy infrastructures in smart grid environments.

关键词： Renewable energy machine learning optimization Genetic algorithms Energy generation forecasting

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QUICwand: A machine learning optimization-based Hybrid Defense Approach Against QUIC Flooding Attacks 20

QUICwand: A Machine Learning Optimization-based Hybrid Defen...

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20th International Conference on the Design of Reliable Communication Networks (DRCN)

作者： Joarder, Y. A. Fung, Carol Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ Canada

ISBN: (纸本)9798350348972

QUIC is an emerging transport layer network protocol which provides enhanced security and performance over traditional transport protocols such as TCP and UDP. QUIC aims to provide improved performance and security compared to TCP + TLS 1.3. It also serves as the foundation for HTTP/3. Nevertheless, QUIC is vulnerable to handshake flooding attacks because of its handshaking mechanism. In this paper, we propose "QUICwand," a State-of-the-Art Network Security framework to defend against QUIC flooding attacks. QUICwand leverages Bayesian optimization within a modified Bloom filter to defend against QUIC flooding attacks. Explicitly, QUICwand detects and mitigates QUIC flooding attacks to reduce false positive rates significantly, as demonstrated in empirical evaluations. A key design in QUICwand is the dynamic optimization of the Bloom Filter, which demonstrates significant improvement in detection rates and false positive rates over its predecessor QUICShield. The machine learning core of QUICwand automatically fine-tunes the defense mechanisms to ensure timely and effective detection and mitigation. Our experimental results demonstrate that QUICwand substantially advances network security and resiliency against QUIC flooding attacks.

关键词： QUIC Flooding Attacks machine learning optimization Bayesian optimization Dynamic Bloom Filter Network Security Anomaly Detection DDoS Mitigation

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Multi-criteria study and machine learning optimization of a novel heat integration for combined electricity, heat, and hydrogen production: Application of biogas-fueled S-Graz plant and biogas steam reforming

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CASE STUDIES IN THERMAL ENGINEERING 2024年 63卷

作者： Haghghi, Maghsoud Abdollahi Feili, Milad Ghaebi, Hadi Athari, Hassan Urmia Univ Sch Engn Dept Mech Engn Orumiyeh Iran Elm Ofann Univ Coll Sci & Technol Dept Mech Engn Orumiyeh Iran Univ Mohaghegh Ardabili Fac Engn Dept Mech Engn POB 179 Ardebil Iran

The current research introduces an environmentally friendly heat design method by employing biogas fuel, aiming to yield electricity, hydrogen, and heating load simultaneously. The proposed arrangement consists of a biogas-powered S-Graz plant and a biogas steam reforming cycle. Although methane-fueled S-Graz plants for multigeneration purposes have been studied in previous studies, research on employing biogas fuel to launch a S-Graz plant and integrating a biogas steam reforming cycle with such a plant has yet to be examined. The model is simulated using the engineering equation solver software, and the study includes thermodynamic, exergoeconomic, and sustainability assessments to show the potential of the suggested configuration. By conducting a sensitivity study, a machine learning optimization method within MATLAB is implemented to exhibit the final optimal solution for the proposed arrangement. This optimization uses artificial neural networks and a non-dominated sorting genetic algorithm-II algorithm in a tripleobjective framework based on energy efficiency, sustainability index, and products' specific cost. The optimization demonstrates that the mentioned objectives reach optimal values of 58.26 %, 4.56, and 15.56 $/GJ, respectively. Also, the optimal net output power and hydrogen production rate equal 5746 kW and 1.45 m3/s, respectively. Besides, the process determines the optimal exergy efficiency, total net present value, and payback period as 52.70 %, 50.3 M$, and 8.96 years, respectively. The total investment cost rate for this system also is found to be 219.8 $/h.

关键词： Environmentally friendly heat design Biogas-powered S -Graz plant Biogas steam reforming Hydrogen machine learning optimization

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A multi-heat recovery approach integrated with LNG regasification in a geothermal-driven multigeneration system: Techno-economic insights and machine learning-based optimization

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APPLIED THERMAL ENGINEERING 2025年 274卷

作者： Ghernaout, Djamel Aljibori, Hakim S. Sultan Samad, Sarminah Algarni, Zaina Amari, Abdelfattah Kadhum, Abdul Amir H. Mahariq, Ibrahim Abdullayeva, Iroda Univ Hail Coll Engn Chem Engn Dept POB 2440 Hail 81441 Saudi Arabia Univ Blida Fac Engn Chem Engn Dept POB 270 Blida 09000 Algeria Univ Warith Al Anbiyaa Coll Engn Karbala 56001 Iraq Princess Nourah Bint Abdulrahman Univ Coll Business Adm Dept Management Riyadh 11671 Saudi Arabia Univ Bisha Fac Sci Bisha Dept Phys POB 199 Bisha 61922 Saudi Arabia King Khalid Univ Coll Engn Dept Chem Engn Abha 61411 Saudi Arabia Univ Al Ameed Karbala Iraq China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan Korea Univ Univ Coll Seoul 02481 South Korea Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Tashkent State Univ Econ Tashkent 100066 Uzbekistan

This study investigates a novel geothermal-based cogeneration system for simultaneous production of electricity, cooling, and hydrogen, addressing the increasing demand for environmentally friendly and effective energy alternatives. The system incorporates a dual-loop Organic Rankine Cycle (ORC) for converting low-grade geothermal energy into power, augmented by Liquefied Natural Gas (LNG) regasification to enhance energy recovery and supply natural gas to residential areas. An integrated Ejector Refrigeration Cycle (ERC) is employed for residential cooling, while a proton exchange membrane electrolyzer (PEME) produces high-purity hydrogen, supporting clean energy initiatives. Thermodynamic and economic evaluations are used to evaluate the system's performance, and parametric and sensitivity studies are also conducted to assess the impact of important design factors. A hybrid optimization framework incorporating artificial neural network and genetic algorithm (ANN-GA) is implemented to increase system efficiency and reduce costs. Key findings reveal that the system, under baseline conditions, generates 4.1 MW of electricity, 14.15 MW of cooling capacity, and 87.6 kg/h of hydrogen, with a levelized cost of electricity (LCOE) of 1.76 cents/kWh and a total cost rate of 310 $/h. optimization improves exergetic efficiency to 25.06 % and reduces the total cost rate to 143.92 $/h. Sensitivity analysis highlights that higher ORC turbine's input pressure improves cooling capacity by 8.14 % but reduces power and hydrogen generation. The combination of ANN-GA decreases optimization process duration from 193 h to 5 min, demonstrating its effectiveness in improving the system's thermodynamic and economic performance. This research offers a comprehensive and optimized solution for sustainable multi-generation energy systems.

关键词： Geothermal cogeneration Organic Rankine cycle (ORC) Liquefied natural gas (LNG) regasification Artificial neural networks (ANNs) machine learning optimization

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Sustainable synthesis of copper oxide nanoparticles using Spondias mombin and biodiesel production from Guizotia abyssinica: Engine performance, emission characteristics, and machine learning-based optimization

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FUEL 2025年 392卷

作者： Abishek, M. S. Kachhap, Sabindra Natl Inst Technol Manipur Dept Mech Engn Imphal 795004 Manipur India

This study aims to develop a sustainable and environmentally friendly fuel alternative by synthesizing CuO nanoparticles using Spondias mombin and blending them with biodiesel derived from Guizotia abyssinica (L.). Several characterizations of nanomaterial are employed to analyse and confirm the structural, morphological, compositional, and optical properties of materials. Biodiesel yield optimization was conducted using machine learning-based optimization (MLO) techniques. Engine performance was evaluated under varying load conditions using test engine. By characterization, it was found that the CuO nanoparticles exhibited particle size ranging from 10 to 75 nm with a polycrystalline structure. The presence of copper, oxygen, and sulfur were confirmed with atomic percentages of 79.59 % and 2.74 % for copper and oxygen, respectively. Cu-O bonds and CuO/Cu2O phases were identified and optical analysis showed declining UV absorption from 193 to 600 nm. The optimized results from MLO yielded optimal results, with ideal M1, M2, and B values of 6.819, 0.217, and 86.07, respectively, Gradient descent and Simulated Annealing Technique provided comparable optimal outcomes;the stochastic technique approach produced optimal values of 16.103, 0.117, and 62.89. The study found that increasing the compression ratio from 16.5 to 18.5 improved brake thermal efficiency (BTE) by 1.39 %. Adding CuO nanoparticles improved BTE by 0.36 %, while biodiesel mixes reduced BTE by 1.53 %. CuO nanoparticles also reduced CO and PM emissions by 11.55 % and 20.24 %, respectively. The MLO technique effectively determined optimal biodiesel production parameters. CuO nanoparticles improve biodiesel performance and emissions, demonstrating potential for sustainable energy applications.

关键词： Spondias mombin Guizotia abyssinica (L.) Nanoparticle characterization Combustion Emission machine learning optimization

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