Showing papers by "Guangji Hu published in 2022"
TL;DR: An integrated probabilistic-fuzzy synthetic evaluation (PFSE) approach was developed for assessing drinking water quality in rural and remote communities (RRCs) through the lens of health risks and aesthetic impacts as discussed by the authors.
9 citations
TL;DR: In this article , the authors presented a novel life cycle assessment-based framework for low-impact offshore oil spill response waste (OSRW) management, which consists of the design of experiment, life-cycle assessment, multi-criteria decision analysis (MCDA), operational cost analysis, and generation of regression models for impact prediction.
7 citations
TL;DR: In this article , an integrated life cycle assessment (LCA)-fuzzy synthetic evaluation (FSE) model is developed to help practitioners select the optimal site remediation plan by incorporating life cycle impacts into the comprehensive suitability evaluation.
4 citations
TL;DR: In this paper , the authors used various regression-based machine learning techniques, including artificial neural networks (ANNs), Gaussian process regression (GPR), and support vector regression, to develop decision-support models for OSRM selection.
4 citations
TL;DR: In this paper , a system dynamics model was developed to estimate the quantity of oily waste generated from marine oil spill response operations, including recovered oil, oily water, oily sorbents, oily personal protection equipment, and oily debris.
Abstract: The understanding of waste generation is of critical importance for effective oily waste management in marine oil spill response operation. A system dynamics model was developed in this study to estimate the quantity of oily waste generated from marine oil spill response operations. Various aspects were considered, including weather conditions, spilled oil volume and characteristics, response time, and response methods. The types of oily waste include recovered oil, oily water, oily sorbents, oily personal protection equipment, and oily debris. The model was validated using data collected from an actual oil spill incident in British Columbia, Canada. The comparison of model estimation and observed results showed an average prediction accuracy of 86%. Sensitivity analysis was conducted to examine the impacts of two modeling parameters, including response arrival time and sorbent booms amount. Results of a case study indicated that initiation of response operations 5-h earlier could increased oil recovery by 26%. Furthermore, sensitivity analysis highlighted a 45% overuse of sorbents which resulted in the generation of unnecessary oily solid waste. Response surface methodology (RSM) analysis was applied to analyze the interaction effect of model parameters on model outputs. Results showed a significant interaction between sea temperature and response arrival time on recovered oil and between sorbent boom weight and sorbent booms usage rate on solid waste. The developed model can provide an effective tool for informed waste management decision-making related to marine oil spill response operations.
2 citations
TL;DR: In this article , eleven machine learning techniques, including three multivariate linear regression-based, three regression tree based, three neural networks-based and two advanced nonparametric regression techniques, are used to develop models for predicting three emerging disinfection byproducts (dichloroacetonitrile, chloropicrin, and trichloropropanone) in small water distribution networks (SWDNs).
1 citations
TL;DR: In this paper , an integrated framework combining life cycle thinking and water quality assessment techniques was developed to evaluate the DWMS' performance in terms of water quality, environment, and economics, and six DWMSs were assessed using the integrated framework as a case study.
1 citations
TL;DR: In this paper , the authors investigated the performance of Dioctyl sodium sulfosuccinate (DSS), a double-chain anionic surfactant, in breaking crude oil-in-water emulsions.
Abstract: This research investigated the performance of dioctyl sodium sulfosuccinate (DSS), a double-chain anionic surfactant, in breaking crude oil-in-water emulsions. The response surface methodology was used to consider the effect of the DSS concentration, oil concentration, and shaking time on demulsification efficiency and obtain optimum demulsification conditions. Further single-factor experiments were conducted to investigate the effects of salinity, crude oil conditions (fresh and weathered), and gravity separation settling time. The results showed that DSS efficiently demulsified stable emulsions under different oil concentrations (500–3000 mg/L) within 15 min shaking time. Increasing DSS concentration to 900 mg/L (critical micelle concentration) increased the demulsification efficiency to 99%. DSS not only improved the demulsification efficiency but also did not impede the demulsifier interfacial adsorption at the oil–water interface due to the presence of the double-chain structure. The low molecular weight enables the homogeneous distribution of DSS molecules in the emulsion, leading to a high demulsification efficiency within 15 min. Analysis of variance results indicated the importance of considering the interaction of oil concentration and shaking time in demulsification. DSS could reduce the total extractable petroleum hydrocarbons in the separated water to <10 mg/L without gravity separation and could achieve promising demulsification performance at high salinity (36 g/L) and various concentrations of fresh and weathered oil. The demulsification mechanism was explained by analyzing the microscopic images and the transmittance of the emulsion. DSS could be an efficient double-chain anionic surfactant in demulsifying stable oil-in-water emulsions.