Thermal performance optimization for housing unit design in a cold region of China

Wang, Shanshan; Liu, Nianxiong; Zhang, Jingyu

doi:10.1080/19401493.2021.1970811

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…The ANN method has a wide range of applications, such as predicting the degradation of methyl green by the Fenton process (Taoufik et al, 2021), classifying electromyography signals (Arı et al, 2019), estimating wind speed (Fang & Wang, 2017), optimizing thermal performance in residential unit design (Wang et al, 2021), modelling the severity of traffic accidents (Wen et al, 2021), predicting the damage state of reinforced concrete buildings (Harirchian et al, 2021), predicting physicochemical properties (Rasam et al, 2019), estimating crop yields (Park et al, 2005), calculating solar radiation (Elizonda et al, 1994) and estimating soil temperature (Kara & Cemek, 2019). From these studies, it is seen that the ANNs are used for learning, association, classification, generalization, prediction, feature recognition and optimization like human intelligence (Şahin et al, 2018; Schmidhuber, 2015).…”

Section: Introductionmentioning

confidence: 99%

Optimization of process parameters for remediation performance on mass removal of tetrachloroethylene source zones by using Taguchi design of experiment and artificial neural network

Şahin

Akyol

Kasap

2022

Water & Environment J

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This study tested the remediation performance of various conditions for pool‐dominated tetrachloroethylene resided in the soil at three different levels of type of flushing agent, flushing agent concentration and flushing rate. The experimental data obtained according to the Taguchi orthogonal array L9 were analysed using the Minitab 17 program. Results of the experimental design analysis indicated that the most effective factor is the flushing agent type, followed by flushing agent concentration and flushing rate, respectively. All the parameters used in the system have a significant effect on the experimental results, and the Taguchi method can explain 97.94% of the experimental data. As a result of the ANN analysis, the R2 values for training, testing and validation were calculated as 0.99942, 0.99795 and 0.99964, respectively. This situation shows the importance of experimental design and selection of appropriate conditions, which is very important for rapid and cost‐effective remediation of contaminated sites.

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Section: Introductionmentioning

confidence: 99%

Optimization of process parameters for remediation performance on mass removal of tetrachloroethylene source zones by using Taguchi design of experiment and artificial neural network

Şahin

Akyol

Kasap

2022

Water & Environment J

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“…Since the mathematical modelling of ANNs by McCulloch and Walter Pitts in 1943, it has been used in many fields such as engineering, medicine, agriculture, sociology and psychology for learning, association, classification, generalization, estimation, feature determination and optimization purposes similar to human intelligence (Elizonda et al, 1994; Fang & Wang, 2017; Harirchian et al, 2022; Kara & Cemek, 2019; Park et al, 2005; Rasam et al, 2019; Rumelhart et al, 1986; Schmidhuber, 2015; Şahin et al, 2018, 2022; Taoufik et al, 2021; Wang et al, 2021; Wen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Multi‐response optimization of process parameters for remediation of tetrachloroethylene pools by surfactants: Application of Taguchi design of experiment and Artificial Neural Network

Şahin

Kasap

Akyol

et al. 2023

Water & Environment J

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Within the scope of the study, the effectiveness of the experimental conditions was tested by performing a multi‐response Taguchi experimental design for the optimization of the minimum cost remediation performance with Tween 80, Methyl beta cyclodextrine (MCD) and Sodium dodecyl sulfate (SDS) from tetrachloroethylene (PCE) contaminated porous media. Tween 80, MCD and SDS were extensively used in cosmetic industry as emulsifier. Both time of remediation and cost of remediation were studied as two separate response variables in three replicate experiments conducted according to the Taguchi L9 orthogonal experimental design. In the multi‐response Taguchi analysis, the sensitivity analysis was performed by systematically changing the weights determined for two separate response variables in the calculation of total loss of quality (TNQLj). Optimum experimental conditions were determined with the help of the calculated multi‐response signal/noise (S/N) ratios (MRSN). The results show that the type of Flushing Agent is the most important factor in optimizing the remediation time and remediation cost for the removal of dense non‐aqueous phase liquid (DNAPL) PCE mass. Flushing rate is considered to be the least contributing factor. Furthermore, the results of the analysis of variance (ANOVA) showed that all parameters used in the system had a significant effect on the experimental results and the Taguchi method could explain 97.15% of the Remediation Time and 92.03% of the Remediation Cost. Afterwards, the data obtained from the experiments performed according to the experimental design were modelled using Artificial Neural Network (ANN) to estimate the remediation performance and remediation cost without performing new experiments.

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Developing a multi-objective optimization model for improving building's environmental performance over the whole design process

Zhou,

Tam,

2023

Building and Environment

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Thermal performance optimization for housing unit design in a cold region of China

Cited by 5 publications

References 51 publications

Optimization of process parameters for remediation performance on mass removal of tetrachloroethylene source zones by using Taguchi design of experiment and artificial neural network

Optimization of process parameters for remediation performance on mass removal of tetrachloroethylene source zones by using Taguchi design of experiment and artificial neural network

Multi‐response optimization of process parameters for remediation of tetrachloroethylene pools by surfactants: Application of Taguchi design of experiment and Artificial Neural Network

Developing a multi-objective optimization model for improving building's environmental performance over the whole design process

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