Pub Date : 2024-11-23DOI: 10.1016/j.cles.2024.100163
Houssem Bouazizi , Maha Benali , Jean-Marc Frayret , Rim Larbi
To fight climate change, the Province of Quebec, Canada, has set targets to reduce greenhouse gas emissions by reducing fossil fuel consumption and integrating biofuel content into gasoline and diesel fuel. Motivated by a real-world case study, this paper presents a novel distributed decision model for designing a symbiotic supply chain network and supporting pricing decisions. A distributed decision-making problem is formulated as a game theoretic approach considering a Stackelberg–Nash equilibrium. A novel mathematical model is proposed to support the decisions of four actors: corn farms, processing depots, pig farms, and biorefineries. In addition to the configuration of a biofuel-based industrial symbiosis, the model offers the possibility of setting purchase prices and supply levels for biomass (corn stover supplied by farms), as well as determining sales prices and production levels for the main product (the cellulosic sugar used for the bioethanol production) and a coproduct (pig feed sold to pig farmers). A three-step optimization process involving the user is proposed to address the computational challenges posed by large design problem instances. The case study of the Province of Quebec is used to evaluate the performance of the proposed resolution approach.
{"title":"Joint Design and Pricing Problem for Symbiotic Bioethanol Supply Chain Network: Model and Resolution Approach","authors":"Houssem Bouazizi , Maha Benali , Jean-Marc Frayret , Rim Larbi","doi":"10.1016/j.cles.2024.100163","DOIUrl":"10.1016/j.cles.2024.100163","url":null,"abstract":"<div><div>To fight climate change, the Province of Quebec, Canada, has set targets to reduce greenhouse gas emissions by reducing fossil fuel consumption and integrating biofuel content into gasoline and diesel fuel. Motivated by a real-world case study, this paper presents a novel distributed decision model for designing a symbiotic supply chain network and supporting pricing decisions. A distributed decision-making problem is formulated as a game theoretic approach considering a Stackelberg–Nash equilibrium. A novel mathematical model is proposed to support the decisions of four actors: corn farms, processing depots, pig farms, and biorefineries. In addition to the configuration of a biofuel-based industrial symbiosis, the model offers the possibility of setting purchase prices and supply levels for biomass (corn stover supplied by farms), as well as determining sales prices and production levels for the main product (the cellulosic sugar used for the bioethanol production) and a coproduct (pig feed sold to pig farmers). A three-step optimization process involving the user is proposed to address the computational challenges posed by large design problem instances. The case study of the Province of Quebec is used to evaluate the performance of the proposed resolution approach.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100163"},"PeriodicalIF":0.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.cles.2024.100162
Md Tasbirul Islam , Sikandar Abdul Qadir , Amjad Ali , Muhammad Waseem Khan
This review article critically examines papers on renewable energy integration (REI), with a specific focus on the economic and environmental impact assessments across multiple sectors, including agriculture, transportation, electricity production, buildings, and biofuel production. A total of 111 articles from the Web of Science Core Collection database were reviewed using a systematic literature review methodology and content analysis techniques. The results indicate that evaluation-type studies, particularly those employing optimization and simulation-based methods, such as techno-economic analysis (TEA) (28 papers) and life cycle assessment (LCA) (20 papers), were the most prominent approaches used for economic and environmental analyses. Optimization techniques such as mixed-integer linear programming (6 papers), genetic algorithms (GA) (5 papers), and particle swarm optimization (PSO) (4 papers) were widely applied. The quantitative analysis of impact assessment indicators shows that REI has yielded significant long-term positive results across multiple RE sources, sectors, and regions. A detailed examination of mathematical models (e.g., optimization techniques) and simulation modeling combined with LCA will assist future researchers in optimizing energy systems and enhancing sustainability in sectors such as agriculture and water desalination. The conceptual inclusion of circular economy within the research field needs to be more present among researchers, and most of the studies focused on technical aspects of RE integration and assessing impacts rather than identifying a systemic change across the sectors. Several future research directions have been identified across sectors, offering opportunities to advance the field. Policymakers will find this paper valuable for informed decision-making and the development of robust policy frameworks.
{"title":"Economic and environmental impact assessment of renewable energy integration: A review and future research directions","authors":"Md Tasbirul Islam , Sikandar Abdul Qadir , Amjad Ali , Muhammad Waseem Khan","doi":"10.1016/j.cles.2024.100162","DOIUrl":"10.1016/j.cles.2024.100162","url":null,"abstract":"<div><div>This review article critically examines papers on renewable energy integration (REI), with a specific focus on the economic and environmental impact assessments across multiple sectors, including agriculture, transportation, electricity production, buildings, and biofuel production. A total of 111 articles from the Web of Science Core Collection database were reviewed using a systematic literature review methodology and content analysis techniques. The results indicate that evaluation-type studies, particularly those employing optimization and simulation-based methods, such as techno-economic analysis (TEA) (28 papers) and life cycle assessment (LCA) (20 papers), were the most prominent approaches used for economic and environmental analyses. Optimization techniques such as mixed-integer linear programming (6 papers), genetic algorithms (GA) (5 papers), and particle swarm optimization (PSO) (4 papers) were widely applied. The quantitative analysis of impact assessment indicators shows that REI has yielded significant long-term positive results across multiple RE sources, sectors, and regions. A detailed examination of mathematical models (e.g., optimization techniques) and simulation modeling combined with LCA will assist future researchers in optimizing energy systems and enhancing sustainability in sectors such as agriculture and water desalination. The conceptual inclusion of circular economy within the research field needs to be more present among researchers, and most of the studies focused on technical aspects of RE integration and assessing impacts rather than identifying a systemic change across the sectors. Several future research directions have been identified across sectors, offering opportunities to advance the field. Policymakers will find this paper valuable for informed decision-making and the development of robust policy frameworks.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.cles.2024.100156
Abdulhalim Musa Abubakar , Lukman Buba Umdagas , Moses NyoTonglo Arowo , Marwea Al-Hedrewy , Mahlon Kida Marvin , Noureddine Elboughdiri , Aminullah Zakariyya Abdul , Jenisus O. Dejarlo , Rezkallah Chafika
The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO2) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO2 and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO2 purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO2 while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.
由于对碳中性能源解决方案的需求日益增长，因此有必要开发二氧化碳（CO2）回收和从湿天然气中生产甜碳中性天然气（CNNG）的高效系统。尽管已有一些方法，但在工艺优化、溶剂效率和产出纯度方面仍存在局限性。本研究旨在利用 Aspen Plus V8.8 中建模的集成式三阶段工艺模拟同时回收 CO2 和 CNNG 的系统，从而弥补这些不足。这项工作的独特之处在于采用 ENRTL-RK 基础模型，并结合敏感性分析来优化 13 个相互连接的工艺单元（包括压缩机、热交换器和萃取塔）的输入参数。主要创新包括采用新颖的装置配置，在最佳条件下，CNNG 的回收效率达到 95.94%，二氧化碳纯度达到 93.185%，超过了传统方法。通过仔细调整输入参数，提高了单乙醇胺（MEA）溶剂的性能，与标准操作设置相比，其吸收效率提高了 12%。敏感性分析表明，进料压力和溶剂流速等关键参数是最大化产出效率的主要驱动因素。这项研究还对动力需求进行了详细的量化评估，在 110 巴排气压力下，压缩机制动马力 (BHP) 为 182605 瓦。通过引入系统的工艺优化方法，该研究填补了现有的研究空白，显著提高了 CNNG 和 CO2 的纯度和回收率，同时最大限度地降低了能耗。研究结果不仅证明了该工艺的可行性，还为进一步完善可持续气体处理技术奠定了基础。
{"title":"Simulation of a system to simultaneously recover CO2 and sweet carbon-neutral natural gas from wet natural gas: A delve into process inputs and units performances","authors":"Abdulhalim Musa Abubakar , Lukman Buba Umdagas , Moses NyoTonglo Arowo , Marwea Al-Hedrewy , Mahlon Kida Marvin , Noureddine Elboughdiri , Aminullah Zakariyya Abdul , Jenisus O. Dejarlo , Rezkallah Chafika","doi":"10.1016/j.cles.2024.100156","DOIUrl":"10.1016/j.cles.2024.100156","url":null,"abstract":"<div><div>The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO<sub>2</sub>) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO<sub>2</sub> and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO<sub>2</sub> purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO<sub>2</sub> while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.cles.2024.100157
Shree Om Bade, Olusegun Stanley Tomomewo
This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m2, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.
{"title":"Optimizing a hybrid wind-solar-biomass system with battery and hydrogen storage using generic algorithm-particle swarm optimization for performance assessment","authors":"Shree Om Bade, Olusegun Stanley Tomomewo","doi":"10.1016/j.cles.2024.100157","DOIUrl":"10.1016/j.cles.2024.100157","url":null,"abstract":"<div><div>This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m<sup>2</sup>, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-12DOI: 10.1016/j.cles.2024.100153
Chukwuemeka Emmanuel Okafor, Komla Agbenyo Folly
This work proposes a design and implementation of a control system for the multifunctional applications of a Battery Energy Storage System in an electric network. Simulation results revealed that through the suggested control approach, a frequency support of 50.24 Hz for the 53-bus system during a load decrease contingency of 350MW was achieved. Without the control system, the frequency was 50 .38Hz. Such a high frequency if not addressed, may result in a loss of synchronization among interconnected synchronous machines which could result in a decrease in voltage stability of the studied network. Besides, a reduction of about 2.05 MW in the active power losses was accomplished and a reactive power support of 3.63Mvar was realised. Thus, through the proposed strategy, Battery energy storage system has been enabled for frequency regulation, power loss minimization and voltage deviation mitigation resulting in an overall enhancement of the power quality of the electric power delivered in the studied networks.
{"title":"Design and implementation of a control system for multifunctional applications of a Battery Energy Storage System (BESS) in a power system network","authors":"Chukwuemeka Emmanuel Okafor, Komla Agbenyo Folly","doi":"10.1016/j.cles.2024.100153","DOIUrl":"10.1016/j.cles.2024.100153","url":null,"abstract":"<div><div>This work proposes a design and implementation of a control system for the multifunctional applications of a Battery Energy Storage System in an electric network. Simulation results revealed that through the suggested control approach, a frequency support of 50.24 Hz for the 53-bus system during a load decrease contingency of 350MW was achieved. Without the control system, the frequency was 50 .38Hz. Such a high frequency if not addressed, may result in a loss of synchronization among interconnected synchronous machines which could result in a decrease in voltage stability of the studied network. Besides, a reduction of about 2.05 MW in the active power losses was accomplished and a reactive power support of 3.63Mvar was realised. Thus, through the proposed strategy, Battery energy storage system has been enabled for frequency regulation, power loss minimization and voltage deviation mitigation resulting in an overall enhancement of the power quality of the electric power delivered in the studied networks.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.cles.2024.100155
Muhammad Rifansyah, Dzikri Firmansyah Hakam
The utilization of solar energy is crucial for the advancement of sustainable power generation on a worldwide scale, driven by environmental concerns and the depletion of fossil fuels. Indonesia's goal is to achieve carbon neutrality by 2060 and it is aggressively advocating for solar energy, which includes the implementation of new methods such as floating photovoltaic (PV) systems. This study evaluates the Techno-Economic Feasibility of Indonesia's Cirata 145 MW floating solar PV project by employing RETScreen technology. The objective is to improve the long-term financial stability, decrease greenhouse gas emissions, and suggest viable choices for improvement. Examining three scenarios that involve alterations in carbon emissions, energy pricing, and loan interest rates demonstrates different levels of project feasibility. The introduction of carbon tax emission pricing has a substantial impact on the feasibility of projects. This study provides useful insights into doing techno-economic feasibility assessments using RETScreen for floating photovoltaic (PV) systems. It demonstrates how modifying parameters can effectively mitigate project risks.
{"title":"Techno economic study of floating solar photovoltaic project in Indonesia using RETscreen","authors":"Muhammad Rifansyah, Dzikri Firmansyah Hakam","doi":"10.1016/j.cles.2024.100155","DOIUrl":"10.1016/j.cles.2024.100155","url":null,"abstract":"<div><div>The utilization of solar energy is crucial for the advancement of sustainable power generation on a worldwide scale, driven by environmental concerns and the depletion of fossil fuels. Indonesia's goal is to achieve carbon neutrality by 2060 and it is aggressively advocating for solar energy, which includes the implementation of new methods such as floating photovoltaic (PV) systems. This study evaluates the Techno-Economic Feasibility of Indonesia's Cirata 145 MW floating solar PV project by employing RETScreen technology. The objective is to improve the long-term financial stability, decrease greenhouse gas emissions, and suggest viable choices for improvement. Examining three scenarios that involve alterations in carbon emissions, energy pricing, and loan interest rates demonstrates different levels of project feasibility. The introduction of carbon tax emission pricing has a substantial impact on the feasibility of projects. This study provides useful insights into doing techno-economic feasibility assessments using RETScreen for floating photovoltaic (PV) systems. It demonstrates how modifying parameters can effectively mitigate project risks.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.cles.2024.100154
Miraduzzaman Chowdhury , Mohammad Shohag Babu , Shahadat Hossain , Rony Mia , Shekh Md. Mamun Kabir
In the industrial range, optimizing dyeing and finishing energy is important to control environmental pollution. In the Dyeing stage to finishing of textiles gas, electricity, steam, and water are used 260 m3/hour, 591 kWh, 1.2 pounds/hour, and 8.69 tons/hour respectively. If textile professionals do not match the desired shade and quality of fabrics with the use of minimal resources the energy cost will be multiple times higher. This study investigates the change in the shade of fleece knitted fabrics from the dyeing unload to the finish stage and assumes a dyeing recipe adjustment, focusing on the impact of optimized dyeing and finishing processes. Also, it focuses on qualitative changes in properties across various color variations. Identical dyeing recipes for light, medium, and dark shades of red, blue, and navy. Properties such as GSM (grams per square meter), width, color strength, shade (darker/lighter, red/green, blue/yellow), shrinkage, spirality, pilling, bursting strength, and color fastness were analyzed. Dyeing to post-finishing, an increase in color strength (K/S) values was observed, with examples including minimum increases from 2.9 to 3.18 for light red and maximum from 19.3 to 22.9 for dark navy shade. Darker shades (DL*) were observed after stenter 1st pass (among all variants, red: 1.2 % to 8.1 %, blue: 4.5 % to 6.7 %, navy: 1.6 % to 2 %), while lighter shades (DL*) were observed following sueding and napping (among all variants, red: 3.1 % to 19.7 %, blue: 11.8 % to 19.7 %, navy: 14.8 % to 27.6 %). Greenish (Da*) and yellowish (Db*) tones are prominent across all colors in the finishing stages. Besides, other properties shrinkage, spirality, pilling, bursting strength, and color fastness significantly changed. These findings offer valuable guidance for dyeing professionals aiming to achieve the desired adjustment of shades that match the quality standard and produce sustainable fleece fabrics. To compensate for the shade lightening that occurs during the finishing process, it is recommended to keep the fabric shade slightly darker (5.70 % to 23.10 %) at the dyeing stage.
{"title":"Optimizing textile dyeing and finishing for improved energy efficiency and sustainability in fleece knitted fabrics","authors":"Miraduzzaman Chowdhury , Mohammad Shohag Babu , Shahadat Hossain , Rony Mia , Shekh Md. Mamun Kabir","doi":"10.1016/j.cles.2024.100154","DOIUrl":"10.1016/j.cles.2024.100154","url":null,"abstract":"<div><div>In the industrial range, optimizing dyeing and finishing energy is important to control environmental pollution. In the Dyeing stage to finishing of textiles gas, electricity, steam, and water are used 260 m<sup>3</sup>/hour, 591 kWh, 1.2 pounds/hour, and 8.69 tons/hour respectively. If textile professionals do not match the desired shade and quality of fabrics with the use of minimal resources the energy cost will be multiple times higher. This study investigates the change in the shade of fleece knitted fabrics from the dyeing unload to the finish stage and assumes a dyeing recipe adjustment, focusing on the impact of optimized dyeing and finishing processes. Also, it focuses on qualitative changes in properties across various color variations. Identical dyeing recipes for light, medium, and dark shades of red, blue, and navy. Properties such as GSM (grams per square meter), width, color strength, shade (darker/lighter, red/green, blue/yellow), shrinkage, spirality, pilling, bursting strength, and color fastness were analyzed. Dyeing to post-finishing, an increase in color strength (K/S) values was observed, with examples including minimum increases from 2.9 to 3.18 for light red and maximum from 19.3 to 22.9 for dark navy shade. Darker shades (DL*) were observed after stenter 1st pass (among all variants, red: 1.2 % to 8.1 %, blue: 4.5 % to 6.7 %, navy: 1.6 % to 2 %), while lighter shades (DL*) were observed following sueding and napping (among all variants, red: 3.1 % to 19.7 %, blue: 11.8 % to 19.7 %, navy: 14.8 % to 27.6 %). Greenish (Da*) and yellowish (Db*) tones are prominent across all colors in the finishing stages. Besides, other properties shrinkage, spirality, pilling, bursting strength, and color fastness significantly changed. These findings offer valuable guidance for dyeing professionals aiming to achieve the desired adjustment of shades that match the quality standard and produce sustainable fleece fabrics. To compensate for the shade lightening that occurs during the finishing process, it is recommended to keep the fabric shade slightly darker (5.70 % to 23.10 %) at the dyeing stage.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.cles.2024.100152
Yubao Wang, Huiyuan Pan, Junjie Zhen, Boyang Xu
This paper quantitatively examines the substitution effects within China's clean energy sector, focusing on the hydropower and new energy generation sectors across the top 14 hydropower-producing provinces, which collectively contribute to over 80 % of the country's total hydropower output. To provide a comprehensive analysis of regions that significantly influence national trends, the study utilizes the Cross-Price Elasticity (CPE) and Morishima Elasticity of Substitution (MES). CPE measures how the quantity demanded of one energy source responds to a change in the price of another, while MES assesses the sensitivity of the ratio between two energy inputs to price changes. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model is employed to forecast energy substitution dynamics, offering robust predictive accuracy. The average MES between clean energy and thermal power is 0.663, indicating a moderate substitution relationship, with the effect more pronounced in summer. Additionally, the mean MES between hydropower and new energy generation is 2.067, reflecting a strong substitution effect between these two clean energy forms. Furthermore, the SARIMA model shows a mean squared error (MSE) as low as 0.0006 in some cases, demonstrating its robust predictive accuracy in forecasting energy substitution dynamics. These results offer empirical support for policies aimed at reducing reliance on thermal power and promoting clean energy development in key provinces.
本文定量研究了中国清洁能源行业的替代效应，重点关注水电产量最高的 14 个省份的水电和新能源发电行业，这 14 个省份的水电产量合计占全国水电总产量的 80% 以上。为了全面分析对全国趋势有重大影响的地区，研究采用了交叉价格弹性 (CPE) 和森岛替代弹性 (MES)。CPE 衡量一种能源的需求量如何对另一种能源的价格变化做出反应，而 MES 则评估两种能源投入之间的比率对价格变化的敏感性。采用季节自回归综合移动平均（SARIMA）模型来预测能源替代动态，具有很高的预测准确性。清洁能源与火力发电之间的平均 MES 为 0.663，表明两者之间存在适度的替代关系，夏季的替代效应更为明显。此外，水力发电与新能源发电之间的平均 MES 为 2.067，反映出这两种清洁能源形式之间存在较强的替代效应。此外，SARIMA 模型在某些情况下的均方误差（MSE）低至 0.0006，这表明该模型在预测能源替代动态方面具有很强的预测准确性。这些结果为重点省份减少对火电的依赖、促进清洁能源发展的政策提供了经验支持。
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Pub Date : 2024-09-29DOI: 10.1016/j.cles.2024.100151
Zuriani Mustaffa , Mohd Herwan Sulaiman
Determining the Remaining Useful Life (RUL) of a battery is essential for several purposes, including proactive maintenance planning, optimizing resource allocation, preventing unforeseen failures, improving safety, extending battery lifespan, and achieving accurate cost savings. Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSONN) for estimating battery RUL. In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset employed for this investigation comprises eight input parameters and one output variable, representing the battery RUL. In conducting an analysis, the performance of the PSONN model is compared with hybrid NN with Cultural Algorithm (CA-NN) and Harmony Search Algorithm (HSA-NN), as well as the standalone Autoregressive Integrated Moving Average (ARIMA). Upon examination of the findings, it becomes evident that the PSONN model outperforms the alternatives with an MAE of 2.7708 and an RMSE of 4.3468, significantly lower than HSA-NN (MAE: 22.0583, RMSE: 34.5154), CA-NN (MAE: 9.1189, RMSE: 22.4646), and ARIMA (MAE: 494.6275, RMSE: 584.3098). The PSONN also achieves the lowest maximum error of 104.7381 compared to 490.3125 for HSA-NN, 827.0163 for CA-NN, and 1,160.0000 for ARIMA. Additionally, the low two-tail probability values (P(T ≤ t)), all below the significance level of 0.05, indicate that the differences between PSONN and the other methods (HSA-NN, CA-NN, and ARIMA) are statistically significant. These results highlight the superior accuracy and robustness of the PSONN model in predicting battery RUL. This study contributes to the field by presenting the PSONN as a highly effective tool for accurate battery RUL estimation, as evidenced by its superior performance over alternative methods.
{"title":"Battery remaining useful life estimation based on particle swarm optimization-neural network","authors":"Zuriani Mustaffa , Mohd Herwan Sulaiman","doi":"10.1016/j.cles.2024.100151","DOIUrl":"10.1016/j.cles.2024.100151","url":null,"abstract":"<div><div>Determining the Remaining Useful Life (RUL) of a battery is essential for several purposes, including proactive maintenance planning, optimizing resource allocation, preventing unforeseen failures, improving safety, extending battery lifespan, and achieving accurate cost savings. Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSO<img>NN) for estimating battery RUL. In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The dataset employed for this investigation comprises eight input parameters and one output variable, representing the battery RUL. In conducting an analysis, the performance of the PSO<img>NN model is compared with hybrid NN with Cultural Algorithm (CA-NN) and Harmony Search Algorithm (HSA-NN), as well as the standalone Autoregressive Integrated Moving Average (ARIMA). Upon examination of the findings, it becomes evident that the PSO<img>NN model outperforms the alternatives with an MAE of 2.7708 and an RMSE of 4.3468, significantly lower than HSA-NN (MAE: 22.0583, RMSE: 34.5154), CA-NN (MAE: 9.1189, RMSE: 22.4646), and ARIMA (MAE: 494.6275, RMSE: 584.3098). The PSO<img>NN also achieves the lowest maximum error of 104.7381 compared to 490.3125 for HSA-NN, 827.0163 for CA-NN, and 1,160.0000 for ARIMA. Additionally, the low two-tail probability values (P(<em>T</em> ≤ <em>t</em>)), all below the significance level of 0.05, indicate that the differences between PSO<img>NN and the other methods (HSA-NN, CA-NN, and ARIMA) are statistically significant. These results highlight the superior accuracy and robustness of the PSO<img>NN model in predicting battery RUL. This study contributes to the field by presenting the PSO<img>NN as a highly effective tool for accurate battery RUL estimation, as evidenced by its superior performance over alternative methods.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-29DOI: 10.1016/j.cles.2024.100149
Mohd Herwan Sulaiman , Zuriani Mustaffa , Mohd Mawardi Saari , Mohammad Fadhil Abas
Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient energy supply, reducing the environmental impact of energy production, and promoting a cleaner and more sustainable energy supply. Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. Therefore, improving the accuracy of wind power generation forecasting is essential for optimizing energy storage and grid management, reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and promoting a more sustainable energy future. This study proposes an innovative approach to enhance wind power generation forecasting accuracy by leveraging the strengths of metaheuristic algorithms for feature selection and integrating them with Neural Networks (NN). Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. This innovative framework advances the field of renewable energy forecasting and provides valuable insights into optimizing feature sets for improved predictions across diverse domains.
{"title":"Wind power forecasting with metaheuristic-based feature selection and neural networks","authors":"Mohd Herwan Sulaiman , Zuriani Mustaffa , Mohd Mawardi Saari , Mohammad Fadhil Abas","doi":"10.1016/j.cles.2024.100149","DOIUrl":"10.1016/j.cles.2024.100149","url":null,"abstract":"<div><div>Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient energy supply, reducing the environmental impact of energy production, and promoting a cleaner and more sustainable energy supply. Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. Therefore, improving the accuracy of wind power generation forecasting is essential for optimizing energy storage and grid management, reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and promoting a more sustainable energy future. This study proposes an innovative approach to enhance wind power generation forecasting accuracy by leveraging the strengths of metaheuristic algorithms for feature selection and integrating them with Neural Networks (NN). Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. This innovative framework advances the field of renewable energy forecasting and provides valuable insights into optimizing feature sets for improved predictions across diverse domains.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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