Article
Chemistry, Multidisciplinary
Jin-Hyun Lee, Young-Hum Cho
Summary: This study successfully achieved the goal of saving building energy and improving comfort through predictive model-based mixed-air temperature optimization.
APPLIED SCIENCES-BASEL
(2022)
Article
Thermodynamics
Akhmad Afandi, Nuraini Lusi, I. G. N. B. Catrawedarma, Subono, Bayu Rudiyanto
Summary: This study used an Artificial Neural Network (ANN) to predict the subsurface temperature and humidity in the Blawan geothermal area, showing that the method is highly accurate and effective.
CASE STUDIES IN THERMAL ENGINEERING
(2022)
Article
Energy & Fuels
Gulce Cakman, Saba Gheni, Selim Ceylan
Summary: The study successfully developed an artificial neural network model to estimate the higher heating value (HHV) of biochars, showing high prediction accuracy compared to previously reported models.
BIOMASS CONVERSION AND BIOREFINERY
(2021)
Article
Energy & Fuels
Fatih Gulec, Direnc Pekaslan, Orla Williams, Edward Lester
Summary: This study applies artificial neural network (ANN) models to predict the higher heating value (HHV) of biomass feedstocks and comprehensively analyzes the factors that affect the prediction, including activation functions, algorithms, hidden layers, dataset, and randomization. The results show that using ANN models trained by the combination of ultimate-proximate analyses (UAPA) datasets, with sigmoidal activation functions (tansig and logsig), and with Levenberg-Marquardt (lm) or Bayesian Regularization (br) algorithms as training activation functions, can provide accurate HHV prediction.
Article
Thermodynamics
Fan Zhang, Chris Bales, Hasan Fleyeh
Summary: District heating systems are widely used in Northern Europe, with Sweden having the largest share of the heat supply market. However, it is common for these systems to fail to achieve expected performance due to various faults or inappropriate operations. A study proposed a bidirectional long short term memory neural network approach with attention mechanism to classify substations using night setback, showing better performance than baseline models in a case study of 10 substations in Sweden.
Article
Biology
Duc Tri Phan, Van Nam Tran, Le Hai Tran, Sumin Park, Jaeyeop Choi, Hyun Wook Kang, Junghwan Oh
Summary: This study proposed a real-time closed-loop system for monitoring and controlling the temperature of photothermal therapy using non-contact infrared thermal sensor array and artificial neural network. The effectiveness of fuzzy control with temperature method in maintaining tissue temperature was validated, showing a linear relationship between coagulation areas and treatment time. Integration of real-time feedback temperature control with predictive ANN demonstrated a feasible approach for precise thermal coagulation in treating papillary thyroid microcarcinoma.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Mathematics
Xiao Tan, Yuan Zhou, Zuohua Ding, Yang Liu
Summary: The study evaluates three different rule extraction methods for artificial neural networks, showing that while the third method can generate fuzzy rules for all data sets, it does not always guarantee accuracy, especially for datasets with poor separability.
Article
Engineering, Chemical
Sam Durairaj, Joanofarc Xavier, Sanjib Kumar Patnaik, Rames C. Panda
Summary: An important step in nonlinear system identification in industrial units is the use of recurrent and convolution-type deep learning methods.Methods such as pH neutralization schemes used in chemical/pharmaceutical/wastewater process units face challenges due to the inherent nonlinear dynamics during the neutralization process. This research proposes a deep Temporal Convolution Network (TCN) with a larger receptive field to learn the dynamics of the pH neutralization process.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Automation & Control Systems
Muhyaddin J. H. Rawa, Mohammad Hossein Razavi Dehkordi, Mohammad Javad Kholoud, Nidal H. Abu-Hamdeh, Hamidreza Azimy
Summary: This study examines the temperature distribution and velocity field during distinctive laser welding processes using numerical simulation. The results show that shear stress and buoyancy force are crucial in the formation of liquid metal flow. A novel artificial intelligence method combining ANN and PSO algorithms is proposed for optimal prediction of melting ratio and maximum temperature.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Environmental
Jiazhi Yang, Xuejun Long, Xiaonan Feng, Jun Wan
Summary: A novel composite material, kaolin/lanthanum carbonate (KLC), was studied for the simultaneous sorption of orthophosphate and phosphonate. It was found that orthophosphate and phosphonate competed for sorption on the surface of KLC. The maximum sorption capacities of both phosphorus species were achieved at pH 4.9, and the impact of ionic strength on sorption capacity was negligible. An artificial neural network (ANN) model was developed and successfully predicted the removal efficiency of orthophosphate and phosphonate.
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING
(2023)
Article
Chemistry, Applied
Guangming Yao, Yajun Zhou, Zongping Li, Qingshu Ma
Summary: The effects of chicken roasting temperature and time on the production of HAAs were studied, and a predictive model based on heating conditions was established. The results showed that the heating conditions greatly influenced the content of HAAs, with higher temperatures and longer roasting times resulting in higher levels of HAAs. The BP-ANN model was found to have a high potential for predicting the production of HAAs under heating conditions.
Article
Automation & Control Systems
Yangxiao Xiang, Henry Shu-Hung Chung, Hongjian Lin
Summary: This article proposes a light implementation scheme for ANN-based EMPC (LISABE) that improves control performance and reduces online computation time and memory resources through an optimized data generation process and an improved ANN structure.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Daming Wang, Zheng John Shen, Xin Yin, Sai Tang, Xifei Liu, Chao Zhang, Jun Wang, Jose Rodriguez, Margarita Norambuena
Summary: In this article, a new approach called ANN-MPC is proposed as a solution to the increasing complexity and demand of computing resources in power electronic applications. The approach uses an artificial neural network to train an MPC controller and eliminates the need for heavy mathematical computation. Simulation and experimental results demonstrate that the FPGA-based ANN-MPC controller can significantly reduce the resource requirement while offering the same control performance as conventional MPC.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Civil
Amlan Kumar Bairagi, Sujit Kumar Dalui
Summary: This study predicted the aerodynamic coefficients on different building models using computational fluid dynamics and artificial neural network methods, and found that the double setback model is more efficient in resisting drag and lift compared to the single setback building. The recommended setback number is crucial in controlling pressure and velocity-induced frequencies.
CANADIAN JOURNAL OF CIVIL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Yifeng Ling, Kejin Wang, Xuhao Wang, Wengui Li
Summary: Fly ash-based geopolymer has been extensively studied due to its comparable properties to Portland cement and environmental benefits. However, the uncertainty and complexity of design parameters make it difficult to create a systematic approach. Artificial neural network models can predict key properties of high-calcium fly ash-based geopolymer, providing guidance for engineering applications.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Energy & Fuels
Eun Ji Choi, Yongseok Yoo, Bo Rang Park, Young Jae Choi, Jin Woo Moon
Article
Energy & Fuels
Bo Rang Park, Eun Ji Choi, Young Jae Choi, Jin Woo Moon
Article
Construction & Building Technology
Young Kwon Yang, Min Young Kim, Min Hee Chung, Jin Chul Park
ENERGY AND BUILDINGS
(2019)
Article
Construction & Building Technology
Jin Woo Moon, Jonghoon Ahn
ENERGY AND BUILDINGS
(2020)
Article
Construction & Building Technology
Min Hee Chung
ADVANCES IN CIVIL ENGINEERING
(2020)
Article
Construction & Building Technology
Jin Woo Moon, Yong-Kyu Baik, Sooyoung Kim
BUILDING AND ENVIRONMENT
(2020)
Article
Thermodynamics
Won Hee Kang, Jong Man Lee, Sang Hun Yeon, Min Kyeong Park, Chul Ho Kim, Je Hyeon Lee, Jin Woo Moon, Kwang Ho Lee
Article
Energy & Fuels
Sung Kwon Jung, Youngchul Kim, Jin Woo Moon
Article
Energy & Fuels
Min Hee Chung
Article
Biochemistry & Molecular Biology
Yong Woo Song, Min Young Kim, Min Hee Chung, Young Kwon Yang, Jin Chul Park
Article
Construction & Building Technology
Bo Rang Park, Min Hee Chung, Jin Woo Moon
Summary: The energy industry is transitioning from centralized to distributed energy systems due to global environmental regulations and the rise of smart grids. Peer-to-peer energy trading allows consumers to trade surplus energy produced from distributed energy resources, with the amount of energy produced and consumed by buildings being key factors in strategy formulation. Installation guidelines for photovoltaic systems and building energy performance levels are proposed to facilitate participation in P2P energy trading, with considerations such as lighting density, cooling efficiency, and plug load density identified as important factors for energy saving in residential buildings.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Construction & Building Technology
Bo Rang Park, Min Hee Chung
Summary: Led by the EU, several countries have committed to carbon neutrality and introduced related legislations. In Korea, the government has announced a roadmap for mandatory zero-energy buildings (ZEBs) due to the significant carbon emissions from the building sector. This study evaluates the energy-saving potential of residential buildings and explores the need for further strengthening ZEB standards to achieve carbon neutrality. It also highlights the importance of considering user behavior and designing policies to encourage energy-saving behavior after the introduction of mandatory ZEBs.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Min Hee Chung, Seong Eun Kim, Yong Woo Song, Jin Chul Park
Summary: The world is working towards achieving zero energy in new buildings and energy savings in existing buildings through renovation. The Korean government is conducting a project to support the renovation of aging small public buildings. This study analyzes the effect of minor renovation and compares buildings that have undergone minor renovation. The results show that the passive + active group had the highest energy-saving effect, while the passive group showed the highest cost-effectiveness. Therefore, minor renovations should consider equipment capacity, energy source, and the formulation of passive strategies to reduce energy demand. This study contributes to the expansion of renovation in existing buildings by verifying the effects of renovation according to energy-saving measures.
ENERGY AND BUILDINGS
(2023)
Article
Energy & Fuels
Min Hee Chung, Bo Rang Park, Eun Ji Choi, Young Jae Choi, Choonyeob Lee, Jongin Hong, Hye Un Cho, Ji Hyeon Cho, Jin Woo Moon
SOLAR ENERGY MATERIALS AND SOLAR CELLS
(2020)