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Paulina Vilela,허성구,황보순호,유창규 한국화학공학회 2020 Korean Journal of Chemical Engineering Vol.37 No.7
A two-objective, two-stage mathematical model was developed considering demand uncertainty and operational risk assessment in constructing a utility supply network for steam generation and steam exchange in a petrochemical industrial park. This study defined two objective functions, the total economic cost and risk cost, where the demand uncertainty enhanced the reliability of the utility network design. The economic and risk cost present a holistic study, where the actual operation cost and additional costs in case of industrial operation failure can be determined. For this, two stages were established for both objective functions, a deterministic stage and a stochastic stage. The deterministic stage fixed the parameter values for the optimization problem, while the stochastic stage included the steam supply-demand uncertainty. A case study of the Yeosu industrial park in South Korea was used to show the feasibility of the proposed method, proposing five scenarios for risk assessment analyses. A Pareto set was drawn, showing the optimal values of the optimization scenarios studied. From the optimization analysis, scenario 5 showed the best utility supply network design providing a more realistic network with a balanced total economic cost and risk cost, which presented the lowest risk operation of all facilities. From scenario 5, the results showed a decrease in economic cost by 65.5% to 67.6% compared to the current situation considering the risk costs for the operational risk.
Vilela, Paulina,Liu, Hongbin,Lee, SeungChul,Hwangbo, Soonho,Nam, KiJeon,Yoo, ChangKyoo Elsevier 2018 Science of the Total Environment Vol.633 No.-
<P><B>Abstract</B></P> <P>The release of silver nanoparticles (AgNPs) to wastewater caused by over-generation and poor treatment of the remaining nanomaterial has raised the interest of researchers. AgNPs can have a negative impact on watersheds and generate degradation of the effluent quality of wastewater treatment plants (WWTPs). The aim of this research is to design and analyze an integrated model system for the removal of AgNPs with high effluent quality in WWTPs using a systematic approach of removal mechanisms modeling, optimization, and control of the removal of silver nanoparticles. The activated sludge model 1 was modified with the inclusion of AgNPs removal mechanisms, such as adsorption/desorption, dissolution, and inhibition of microbial organisms. Response surface methodology was performed to minimize the AgNPs and total nitrogen concentrations in the effluent by optimizing operating conditions of the system. Then, the optimal operating conditions were utilized for the implementation of control strategies into the system for further analysis of enhancement of AgNPs removal efficiency. Thus, the overall AgNP removal efficiency was found to be slightly higher than 80%, which was an improvement of almost 7% compared to the BSM1 reference value. This study provides a systematic approach to find an optimal solution for enhancing AgNP removal efficiency in WWTPs and thereby to prevent pollution in the environment.</P> <P><B>Highlights</B></P> <P> <UL> <LI> An ASM-AgNP model is proposed and optimized maximizing the removal efficiency of silver nanoparticles. </LI> <LI> The system operation and cost in control performance are analyzed to investigate the impact of its implementation. </LI> <LI> Various scenarios are studied for evaluating the removal efficiency capacity of the system with silver nanoparticles. </LI> <LI> The highest silver nanoparticles removal efficiency obtained was of 80% with the optimal operating conditions of the model. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Loy-Benitez, Jorge,Vilela, Paulina,Li, Qian,Yoo, ChangKyoo Academic Press 2019 Ecotoxicology and environmental safety Vol.169 No.-
<P><B>Abstract</B></P> <P>Particulate matter with aerodynamic diameter less than 2.5 µm (PM<SUB>2.5</SUB>) in indoor public spaces such as subway stations, has represented a major public health concern; however, forecasting future sequences of quantitative health risk is an effective method for protecting commuters’ health, and an important tool for developing early warning systems. Despite the existence of several predicting methods, some tend to fail to forecast long-term dependencies in an effective way. This paper aims to implement a multiple sequences prediction of a comprehensive indoor air quality index (CIAI) traced by indoor PM<SUB>2.5</SUB>, utilizing different structures of recurrent neural networks (RNN). A standard RNN (SRNN), long short-term memory (LSTM) and a gated recurrent unit (GRU) structures were implemented due to their capability of managing sequential, and time-dependent data. Hourly indoor PM<SUB>2.5</SUB> concentration data collected in the D-subway station, South Korea, were utilized for the validation of the proposed method. For the selection of the most suitable predictive model (i.e. SRNN, LSTM, GRU), a point-by-point prediction on the PM<SUB>2.5</SUB> was conducted, demonstrating that the GRU structure outperforms the other RNN structures (RMSE = 21.04 µg/m<SUP>3</SUP>, MAPE = 32.92%, R<SUP>2</SUP> = 0.65). Then, this model is utilized to sequentially predict the concentration and quantify the health risk (i.e. CIAI) at different time lags. For a 6-h time lag, the proposed model exhibited the best performance metric (RMSE = 29.73 µg/m<SUP>3</SUP>, MAPE = 29.52%). Additionally, for the rest of the time lags including 12, 18 and 24 h, achieved an acceptable performance (MAPE = 29–37%).</P> <P><B>Highlights</B></P> <P> <UL> <LI> A quantitative health risk prediction is used as a tool for the early abnormal detection of indoor PM<SUB>2.5</SUB>. </LI> <LI> Various RNN structures with memory cells are used for the sequential quantitative health risk prediction for PM<SUB>2.5</SUB> effects. </LI> <LI> Performance metrics showed that the most suitable RNN structure is the GRU. </LI> <LI> Forecasting of CIAI is conducted sequentially at different time lags, including 6, 12, 18 and 24 h. </LI> <LI> Results showed that sequential prediction is suitable even for long time lags and future time steps. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>