Guohe Huang

TL;DR: Results from postoptimization simulation show that the carcinogenic risk would decrease to satisfy the regulated risk standard under the given remediation policies and in designing a robust groundwater remediation system.

Abstract: [1] An integrated simulation-optimization system was developed for supporting decisions of the dual phase vacuum extraction (DPVE) processes. The system coupled a DPVE process simulator, a multivariate regression tool and a nonlinear optimization model into a general framework. A stepwise-cluster-analysis technique was provided for establishing a DPVE process forecasting system for describing the relationships between remediation actions and system responses (i.e., total extracted volume of oil/water, elevation distribution of water table, and specific volume of oil). The forecasting system was then embedded into a multiobjective optimization framework, where the objectives were to minimize the operation cost and maximize the remediation efficiency. The constraints include environmental, hydraulic and technical restrictions to the DPVE processes. A case study was conducted for a petroleum-contaminated site in western Canada. The results from the stepwise cluster analysis indicated that the generated cluster trees could be used for predicting system responses of the DPVE process, given inputs of the operating conditions. The prediction accuracies of the generated cluster trees were verified using randomly generated data sets. The optimum operating conditions could vary significantly under different cost-efficiency targets. When a stricter environmental target (i.e., the amount of pollutants in subsurface) was concerned, a higher system cost had to be paid; when the cost became a critical factor, the performance of contaminant removal would have to be compromised. The developed system could be used to analyze tradeoffs between system cost and process efficiency in the DPVE operations; it could also support the formulation of an on-site process-control system with vacuum levels and extraction rates being the main control variables.

TL;DR: An interval-parameter fuzzy-stochastic programming approach is developed for planning air quality management systems under uncertainty and could provide more feasible control strategies under different upcoming pollutant amounts, which was seldom considered in the previous research efforts.

TL;DR: The proposed model presents satisfactory capabilities in analyzing complicated interrelationships among municipal solid waste management, climate-change impact, and environmental pollution control and can also provide optimal allocation schemes and facilitate decision-makers regulating environmentally sustainable strategies.

TL;DR: In this article, the feasibility of treating oily wastewater using synthetic polymers (polyaluminum chloride (PAC), polyferric sulfate (PFS), and polyacrylamide (PAM)) combined with natural diatomite and to refine the operating parameters using diatomites as an adsorbent and a coagulant aid.