Article
Multidisciplinary Sciences
Zhengcai Li, Xinmin Hu, Chun Chen, Chenyang Liu, Yalu Han, Yuanfeng Yu, Lizhi Du
Summary: This paper investigates the optimization algorithms based on machine learning for settlement prediction. By comparing the performance of different algorithms, the study finds that Sparrow Search Algorithm (SSA) significantly improves the optimization effect of the gradient descent model and enhances its stability to a certain degree.
SCIENTIFIC REPORTS
(2022)
Article
Energy & Fuels
Shanhui Zhao, Wanjun Xu, Linghai Chen
Summary: The experimental results showed that the reaction mechanism of oxidative pyrolysis was complex and influenced by the addition of oxygen. Using a combination of logsig and purelin as the transfer function for the hidden layer was suitable for modeling. Particle swarm optimization (PSO) significantly increased the prediction accuracy of the ANN model.
Article
Thermodynamics
Ali Sohani, Siamak Hoseinzadeh, Saman Samiezadeh, Ivan Verhaert
Summary: An enhanced design for a solar still desalination system was employed to develop artificial neural network (ANN) models, with FF and RBF types identified as the best structures for predicting distillate production and water temperature. Error analysis on data not used for ANN model development showed varying errors in different months.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2022)
Article
Environmental Studies
Yoochan Kim, Apurna Ghosh, Erkan Topal, Ping Chang
Summary: Future prediction of commodity price is crucial for mining investors and operators. This research evaluated five different estimation techniques and found that the purelin model using Levenberg-Marquardt technique exhibited the best forecast results for iron ore prices. The accuracy of the forecasts was particularly high for up to 2 months ahead.
Article
Computer Science, Information Systems
Israr Ullah, Muhammad Fayaz, Muhammad Aman, Dohyeun Kim
Summary: The greenhouse industry has experienced significant growth globally, but high energy consumption and labor cost remain major challenges. This article proposes an IoT-based smart solution that integrates key components and uses artificial neural network-based learning modules to enhance prediction and optimization. Experimental analysis and comparative results show that the proposed model achieves reduced energy consumption and cost while maintaining the desired indoor environment for maximizing plant production.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Burak Gulmez
Summary: The stock market is a financial market where shares of publicly listed corporations are bought and sold, and it reflects a country's economic health. Investing in stocks carries risks, but it has the potential for significant long-term returns. Artificial intelligence, including the stock market, is increasingly prevalent in the financial sector.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Sciences
Hongxing Liu, Junxia Li, Hailong Cao, Xianjun Xie, Yanxin Wang
Summary: This study developed an artificial neural network model to predict high iodine levels in groundwater in China and produced a high-resolution nationwide prediction map of high-iodine groundwater. The model showed good accuracy and effectiveness, which can help guide groundwater quality management.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Automation & Control Systems
Wenbo Wang, Yingfeng Zhang, Jinan Gu, Jin Wang
Summary: With the application of IIoT and CPS technologies, the manufacturing resources assignment has transformed from manual and passive mode to intelligent and active mode. A proactive manufacturing resources assignment method based on production performance prediction for the smart factory is proposed, which can accurately predict future production status and assign resources before production exceptions happen.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Green & Sustainable Science & Technology
Syed Asad A. Bokhari, Seunghwan Myeong
Summary: This study investigates the relationships between artificial intelligence (AI), social innovation (SI), and smart decision-making (SDM), and finds a strong mediating effect of SI between the relationship of AI and SDM. The findings have important implications for building smart cities by involving social innovations in decision-making.
Article
Construction & Building Technology
Chujie Lu, Sihui Li, Zhengjun Lu
Summary: This article surveys the research on using artificial neural networks (ANNs) for building energy prediction and summarizes the details and applications of twelve ANN architectures in this field. The article also discusses the challenges of choosing ANN architecture, improving prediction performance, and dealing with the lack of building energy data.
ENERGY AND BUILDINGS
(2022)
Article
Engineering, Civil
Ankita P. Dadhich, Rohit Goyal, Pran N. Dadhich
Summary: The study utilized geospatial techniques and mathematical models to analyze groundwater level and quality changes in 171 villages of Phagi tehsil, Jaipur district from 2012 to 2019. By using Kriging interpolation method and Artificial Neural Network models, accurate predictions for future groundwater levels and quality were made, highlighting potential risks and areas for sustainable groundwater management.
WATER RESOURCES MANAGEMENT
(2021)
Article
Environmental Sciences
Victor O. K. Li, Jacqueline C. K. Lam, Jiahuan Cui
Summary: This article discusses the role and challenges of AI and big data technologies in environmental decision-making, raises a series of important questions, and summarizes the significance and innovation of the articles included in the special issue. It also highlights the important principles of AI for social good.
ENVIRONMENTAL SCIENCE & POLICY
(2021)
Article
Energy & Fuels
Jindai Zhang, Jinlou Zhao
Summary: This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. The methodology constructs dynamic nonlinear programming models and presents analytical solutions for the decision-making issues. The impact of price and demand prediction on sequential optimization is analyzed using numerical analysis.
Article
Green & Sustainable Science & Technology
Djamal Eddine Ghersi, Khaled Loubar, Meriem Amoura, Mohand Tazerout
Summary: The study aims to optimize the performance of a stationary SI engine using artificial neural network models, multi-objective optimization concept, and decision-making approaches to reduce NOx emissions and maintain low biogas upgrading levels. The optimization process successfully achieved the best compromise between high engine performance and low NOx emissions without the need to enhance biogas upgrading.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Environmental Sciences
Eduardo Eiji Maeda, Paivi Haapasaari, Inari Helle, Annukka Lehikoinen, Alexey Voinov, Sakari Kuikka
Summary: Modeling is crucial for modern science and policy making, and artificial intelligence can enhance accuracy but may lack transparency. Participatory methods are suggested to bridge the gap between complex scientific methods and public understanding.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2021)
Article
Chemistry, Multidisciplinary
Thai Ngan Do, Chanhee You, Jiyong Kim
Summary: In this study, a framework for CO2 capture and utilization for energy products (CCU4E) was developed and examined for its techno-economic and environmental performance. A total of 72 CO2-to-fuel pathways were analyzed to understand the trade-off between economic output and environmental impact, with a focus on the role of H2 in mitigating CO2eq emissions and economic potential. The study also highlighted the significance of green hydrogen price as a key factor in future CCU4E for reducing CO2eq emissions at a more stable and lower cost.
ENERGY & ENVIRONMENTAL SCIENCE
(2022)
Article
Chemistry, Multidisciplinary
Hyunwoo Kim, Jiyong Kim, Wangyun Won
Summary: This study developed an integrative process for the co-production of FDCA and furfural as well as activated carbon and evaluated its economic feasibility and environmental sustainability. The proposed process reduces utility consumption and shows comparable economic and environmental performance to traditional processes.
Article
Thermodynamics
Wangyun Won, Jiyong Kim, Thai Ngan Do, Young Gul Hur, Ha Eun Jeong, Jin Woo Chung
Summary: This study aims to develop novel Gas-to-Liquid (GTL) processes for gasoline-equivalent products and compare them with other well-known GTL technological routes. The research overcame challenges in ensuring optimal feed for DME synthesis by exploring different combinations of methane reforming and syngas conditioning processes. The combined steam and CO2 reforming (CSCR) showed superior performance in DTG, with good economic and environmental benefits.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Energy & Fuels
Changsu Kim, Jiyong Kim
Summary: In this study, four different ANNs were used for predicting the performance of Pt-based catalysts in water gas shift reaction, with the multilayer perceptron model showing the best performance. It was demonstrated how selecting the optimal ANN structure can improve prediction accuracy and reduce computational load.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Changsu Kim, Jiyong Kim
Summary: In this study, a machine learning-based approach was developed to predict the performance of Pt/CexZr1-xO2 catalysts in water-gas shift reaction (WGSR). The study identified the key properties of the support material, such as reducibility and thermal stability, that determine the catalyst performance.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Min-Young Oh, Gyuhyung Jin, Bomsock Lee, Jiyong Kim, Wangyun Won
Summary: An integrated process using separation and catalytic conversion technology was designed to co-produce adipic acid (ADA) and 1,4-pentanediol (1,4-PDO) from corn stover, as an effort to replace fossil-based feedstocks and mitigate environmental impacts. The process reduces energy consumption and has economic stability against variable market conditions, making it economically and environmentally feasible.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Engineering, Chemical
Hweeung Kwon, Thai Ngan Do, Wangyun Won, Jiyong Kim
Summary: This study presents a new optimization-based framework for the optimal operation of the naphtha cracking process under uncertain market conditions. It includes a product price prediction model and a naphtha cracking model, aiming to maximize operating profit. Based on price prediction and unit operation conditions, the optimization model determines the major operation conditions of the naphtha cracking process and improves annual profit.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2022)
Article
Computer Science, Interdisciplinary Applications
Thai Ngan Do, Chanhee You, Hegwon Chung, Jiyong Kim
Summary: In this study, an optimization-based framework was developed to determine the optimal strategies for CO2-to-fuel production. The results suggest that direct catalytic hydrogenation is the most economical and environmentally friendly pathway for MeOH, gasoline, and DME, while FT fuels are not recommended.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Green & Sustainable Science & Technology
Hweeung Kwon, Thai Ngan Do, Jiyong Kim
Summary: This study aims to develop heat integration strategies for the production process of liquid hydrogen, including LNG evaporation, utilization of LNG cold energy, and heat recovery from hot process streams. The installation of heat exchanger networks can improve energy efficiency, reduce costs, and consider crucial integration points. The optimal process, which incorporates all heat integration strategies, achieves the best energy efficiency, cost-effectiveness, and environmental friendliness.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Biotechnology & Applied Microbiology
Seo-Young Park, Sun-Jong Kim, Cheol-Hwan Park, Jiyong Kim, Dong-Yup Lee
Summary: Recent advances in process analytical technology and artificial intelligence have made it possible to generate large culture data sets from biomanufacturing processes. Exploiting these data sets is crucial for improving the reliability and efficiency of RTP-producing culture processes and reducing potential faults.
BIOTECHNOLOGY AND BIOENGINEERING
(2023)
Article
Thermodynamics
Thai Ngan Do, Hweeung Kwon, Minseong Park, Changsu Kim, Yong Tae Kim, Jiyong Kim
Summary: This study proposes a carbon-neutral hydrogen production process from natural gas using electrified steam methane reforming (e-SMR) with renewable electricity. Compared to conventional fired-heating steam methane reforming (f-SMR), e-SMR consumes less natural gas, has a higher energy efficiency of 78.7%, lower net CO2eq emissions, and captures CO2. However, it has a higher unit production cost of $3.49/kg H2. Overall, the study highlights the potential and economic viability of e-SMR. Rating: 8/10
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Energy & Fuels
Seolhee Cho, Thai Ngan Do, Jiyong Kim
Summary: This study proposes two advanced processes for methanol production using waste CO2 and renewable H-2 as feedstock, and compares their technical, economic, and environmental performances with conventional processes.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Thai Ngan Do, Young Gul Hur, Hegwon Chung, Jiyong Kim
Summary: This study aims to develop and evaluate the techno-economic-environmental performance of residue gas-to-gasoline hydrocarbon processes (rGTL) via the dimethyl ether-to-gasoline (DTG) route for various feedstocks. It compares different strategies and routes of gasoline synthesis, and identifies the DTG route as the best technological route for residue gas-based gasoline. The study also investigates the industrial and social impact of rGTL and suggests that implementing rGTL can contribute to sustainable development goals in different countries.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Proceedings Paper
Automation & Control Systems
Changsu Kim, Thai Ngan Do, Jiyong Kim
Summary: In this study, the contribution of ALD process features on device film thickness was analyzed using principal component analysis (PCA) method. Highly sensitive features for ALD film growth were identified and a strategy for controlling film thickness was suggested. The understanding on film growth control was improved by the high dimension analysis of ALD process.
Proceedings Paper
Automation & Control Systems
Thai Ngan Do, Hegwon Chung, Yunjik Lee, Changsu Kim, Beomsoo Kim, Jiyong Kim
Summary: This study develops an optimization-based framework for CO2 utilization strategies and analyzes the technical, economic, and environmental performance of CO2-to-fuel strategies. By generating a superstructure of CO2 utilization pathways and employing process simulation and optimization models, the study identifies the optimal CO2 utilization strategy and provides a decision-making guide for policymakers and stakeholders.
