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Baskar G, Nashath Omer S, Saravanan P, Rajeshkannan R, Saravanan V, Rajasimman M, Shanmugam V. Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques. CHEMOSPHERE 2024; 362:142477. [PMID: 38844107 DOI: 10.1016/j.chemosphere.2024.142477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/24/2024] [Accepted: 05/27/2024] [Indexed: 06/27/2024]
Abstract
The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.
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Affiliation(s)
- Gurunathan Baskar
- Department of Biotechnology, St. Joseph's College of Engineering, Chennai, 600119. India; School of Engineering, Lebanese American University, Byblos, 1102 2801, Lebanon.
| | - Soghra Nashath Omer
- School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
| | - Panchamoorthy Saravanan
- Department of Petrochemical Technology, UCE - BIT Campus, Anna University, Tiruchirappalli, Tamil Nadu, 620024, India
| | - R Rajeshkannan
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - V Saravanan
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - M Rajasimman
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - Venkatkumar Shanmugam
- School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
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Ye Y, Guo W, Ngo HH, Wei W, Cheng D, Bui XT, Hoang NB, Zhang H. Biofuel production for circular bioeconomy: Present scenario and future scope. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 935:172863. [PMID: 38788387 DOI: 10.1016/j.scitotenv.2024.172863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/22/2024] [Accepted: 04/27/2024] [Indexed: 05/26/2024]
Abstract
In recent years, biofuel production has attracted considerable attention, especially given the increasing worldwide demand for energy and emissions of greenhouse gases that threaten this planet. In this case, one possible solution is to convert biomass into green and sustainable biofuel, which can enhance the bioeconomy and contribute to sustainable economic development goals. Due to being in large quantities and containing high organic content, various biomass sources such as food waste, textile waste, microalgal waste, agricultural waste and sewage sludge have gained significant attention for biofuel production. Also, biofuel production technologies, including thermochemical processing, anaerobic digestion, fermentation and bioelectrochemical systems, have been extensively reported, which can achieve waste valorization through producing biofuels and re-utilizing wastes. Nevertheless, the commercial feasibility of biofuel production is still being determined, and it is unclear whether biofuel can compete equally with other existing fuels in the market. The concept of a circular economy in biofuel production can promote the environmentally friendly and sustainable valorization of biomass waste. This review comprehensively discusses the state-of-the-art production of biofuel from various biomass sources and the bioeconomy perspectives associated with it. Biofuel production is evaluated within the framework of the bioeconomy. Further perspectives on possible integration approaches to maximizing waste utilization for biofuel production are discussed, and what this could mean for the circular economy. More research related to pretreatment and machine learning of biofuel production should be conducted to optimize the biofuel production process, increase the biofuel yield and make the biofuel prices competitive.
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Affiliation(s)
- Yuanyao Ye
- School of Environmental Science and Engineering, Huazhong University of Science and Technology, No. 1037 Luoyu Road, Wuhan 430074, PR China; Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, PR China
| | - Wenshan Guo
- Centre for Technology in Water and Wastewater, University of Technology Sydney, NSW 2007, Australia
| | - Huu Hao Ngo
- Centre for Technology in Water and Wastewater, University of Technology Sydney, NSW 2007, Australia.
| | - Wei Wei
- Centre for Technology in Water and Wastewater, University of Technology Sydney, NSW 2007, Australia
| | - Dongle Cheng
- College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
| | - Xuan Thanh Bui
- Key Laboratory of Advanced Waste Treatment Technology, Faculty of Environment & Natural Resources, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh (VNU-HCM), Ho Chi Minh City 70000, Viet Nam
| | - Ngoc Bich Hoang
- Institute of Environmental Sciences, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam
| | - Huiying Zhang
- College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
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Ghasemkhani B, Kut RA, Yilmaz R, Birant D, Arıkök YA, Güzelyol TE, Kut T. Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities. SENSORS (BASEL, SWITZERLAND) 2024; 24:4313. [PMID: 39001093 PMCID: PMC11244009 DOI: 10.3390/s24134313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability.
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Affiliation(s)
- Bita Ghasemkhani
- Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey
| | - Recep Alp Kut
- Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey; (R.A.K.); (D.B.)
| | - Reyat Yilmaz
- Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir 35390, Turkey;
| | - Derya Birant
- Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey; (R.A.K.); (D.B.)
| | - Yiğit Ahmet Arıkök
- General Directorate, Gdz Electricity Distribution, Izmir 35042, Turkey; (Y.A.A.); (T.E.G.)
| | - Tugay Eren Güzelyol
- General Directorate, Gdz Electricity Distribution, Izmir 35042, Turkey; (Y.A.A.); (T.E.G.)
| | - Tuna Kut
- Semafor Teknoloji, Dokuz Eylul Technology Development Zone (DEPARK), Izmir 35330, Turkey;
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Alfarra F, Ozcan HK, Cihan P, Ongen A, Guvenc SY, Ciner MN. Artificial intelligence methods for modeling gasification of waste biomass: a review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:309. [PMID: 38407668 DOI: 10.1007/s10661-024-12443-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 02/12/2024] [Indexed: 02/27/2024]
Abstract
Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.
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Affiliation(s)
- Fatma Alfarra
- Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey.
| | - H Kurtulus Ozcan
- Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey
| | - Pınar Cihan
- Corlu Engineering Faculty, Department of Computer Engineering, Tekirdag Namık Kemal Universtiy, 59860, Çorlu, Tekirdag, Turkey
| | - Atakan Ongen
- Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey
| | - Senem Yazici Guvenc
- Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Istanbul, Turkey
| | - Mirac Nur Ciner
- Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey
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Olawuni OA, Sadare OO, Moothi K. The adsorption routes of 4IR technologies for effective desulphurization using cellulose nanocrystals: Current trends, challenges, and future perspectives. Heliyon 2024; 10:e24732. [PMID: 38312585 PMCID: PMC10835247 DOI: 10.1016/j.heliyon.2024.e24732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 01/12/2024] [Indexed: 02/06/2024] Open
Abstract
The combustion of liquid fuels as energy sources for transportation and power generation has necessitated governments worldwide to direct petroleum refineries to produce sulphur-free fuels for environmental sustainability. This review highlights the novel application of artificial intelligence for optimizing and predicting adsorptive desulphurization operating parameters and green isolation conditions of nanocellulose crystals from lignocellulosic biomass waste. The shortcomings of the traditional modelling and optimization techniques are stated, and artificial intelligence's role in overcoming them is broadly discussed. Also, the relationship between nanotechnology and artificial intelligence and the future perspectives of fourth industrial revolution (4IR) technologies for optimization and modelling of the adsorptive desulphurization process are elaborately discussed. The current study surveys different adsorbents used in adsorptive desulphurization and how biomass-based nanocellulose crystals (green adsorbents) are suitable alternatives for achieving cleaner fuels and environmental sustainability. Likewise, the present study reports the challenges and potential solutions to fully implementing 4IR technologies for effective desulphurization of liquid fuels in petroleum refineries. Hence, this study provides insightful information to benefit a broad audience in waste valorization for sustainability, environmental protection, and clean energy generation.
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Affiliation(s)
- Oluwagbenga A Olawuni
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
| | - Olawumi O Sadare
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
- Department of Chemical Engineering, Water Innovation and Research Centre (WIRC), University of Bath, Claveton Down, Bath, North East Somerset, BA27AY, South West, United Kingdom
| | - Kapil Moothi
- Department of Chemical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
- School of Chemical and Minerals Engineering, Faculty of Engineering, North-West University, Potchefstroom, 2520, South Africa
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Wei X. Sustainability metrics of environmental sustainability in Iranian manufacturing sector: achieving through human resources. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:118352-118365. [PMID: 37910352 DOI: 10.1007/s11356-023-30583-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023]
Abstract
The paper investigates the impact of green human resource management (GHRM) on operatives' green actions in Iranian businesses. GHRM is a human resource management approach that prioritizes environmental and social responsibility. The study uses a random sample of 385 managers and employees from Iran's industrialized businesses in all regions. The research uses partial least square structure equations modeling to evaluate the suggested framework. The results show that GHRM practices affect corporate social responsibility, green attitudes inside the workplace, and green activities taken by workers. In addition, a green mentality and a commitment to corporate social responsibility encourage environmental activities in the workplace. The green psychosocial environment and corporate social responsibilities are intermediaries between green human resources development and individual green behaviour in Iranian businesses. The study suggests that incorporating sustainability metrics into the human resource management system is vital for achieving a sustainable future in industrial development. The research has important implications for businesses around the world, particularly those in the manufacturing sector, by encouraging them to adopt more environmentally responsive methods, including reducing resource usage.
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Affiliation(s)
- Xia Wei
- School of Business, Wuxi Taihu University, Wuxi, 214000, Jiangsu, China.
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Moghadasi M, Ghadamian H, Moghadasi M, Seidabadi L. Prediction of outlet air characteristics and thermal performance of a symmetrical solar air heater via machine learning to develop a model-based operational control scheme-an experimental study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:27175-27190. [PMID: 36378379 DOI: 10.1007/s11356-022-24169-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
This study develops reliable and robust machine learning (ML) models to predict the outlet air temperature and humidity and thermal efficiency of a solar air heater (SAH). Also, the application of predictive models for optimal control of the SAH operation is proposed. For this, the work contains three main parts: (a) a vertically-mounted symmetrical SAH was installed outside of a building room and operated throughout the winter of 2022. (b) By conducting experiments for five air mass flow rates, a large dataset with more than 62,500 sample points was collected. (c) Six input features containing time, environmental-related attributes, and SAH variables were applied to develop several state-of-the-art ML algorithms. To figure out the most accurate models for predicting output variables, the dataset was partitioned into three parts. Also, various modeling performance evaluation criteria were calculated and compared on the validation and test sets. Among these models, the gradient boosting machine algorithm based on LightGBM implementation achieved the best degree of generalization in modeling the target variables. The results demonstrated that the developed models obtained the lowest R-squared and the highest mean absolute percentage error of 0.9827 and 2.95%, respectively, on the test set. Moreover, the offline analysis of SAH operation based on the proposed control scheme demonstrated that 350 kWh of thermal energy can be generated during the application in the one-year winter season, 24% more than SAH operation without a model-based control strategy.
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Affiliation(s)
- Meisam Moghadasi
- Department of Energy, Materials and Energy Research Center, Karaj, Iran
| | - Hossein Ghadamian
- Department of Energy, Materials and Energy Research Center, Karaj, Iran.
| | | | - Leila Seidabadi
- Department of Energy, Materials and Energy Research Center, Karaj, Iran
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Engel E, Engel N. A Review on Machine Learning Applications for Solar Plants. SENSORS (BASEL, SWITZERLAND) 2022; 22:9060. [PMID: 36501762 PMCID: PMC9738664 DOI: 10.3390/s22239060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/20/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant's system; proposes a simple but effective integration scheme of an ML sensor solar plant system's implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.
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Sharma K, Dwivedi YK, Metri B. Incorporating causality in energy consumption forecasting using deep neural networks. ANNALS OF OPERATIONS RESEARCH 2022:1-36. [PMID: 35967838 PMCID: PMC9362444 DOI: 10.1007/s10479-022-04857-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.
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Affiliation(s)
- Kshitij Sharma
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yogesh K. Dwivedi
- Emerging Markets Research Centre (EMaRC), School of Management, Swansea University, Room #323, Bay Campus, Fabian Bay, Swansea, SA1 8EN Wales, UK
- Department of Management, Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, Maharashtra India
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