| Literature DB >> 30077839 |
Sha Long1, Lin Zhao2, Hongbo Liu2, Jingchen Li1, Xia Zhou1, Yunfeng Liu1, Zhi Qiao1, Yingxin Zhao1, Yongkui Yang3.
Abstract
Wastewater generated from an industrial park is usually characterized by large volumes, variation in composition, and high pollutant concentrations, and is generally toxic and difficult to biodegrade. Wastewater treatment at an industrial park includes several stages, namely, pretreatment inside factories (F-WWTPs), centralized wastewater treatment (C-WWTP), and reclaimed wastewater treatment (R-WWTP), during which the treatment efficiencies are mutually restricted. Therefore, water pollution control in industrial parks is extremely challenging. In this study, models, including those for pollutant reduction and operating costs, were established considering the F-WWTPs, C-WWTP, and R-WWTP stages at an industrial park. A Monte Carlo model was used to simulate the treatment and solve the above-mentioned models. Consequently, the characteristic values, including the extent of pollutant reduction, concentration of pollutants in the effluent, and operation costs, were predicted under optimal operating conditions of the wastewater treatment system. The established model was verified and applied to industrial park A in the Tianjin Economic-Technological Development Area in China. Based on the comparison of the above-mentioned optimization values with the sampled values as well as the theoretical analysis, the status of the wastewater treatment system in the industrial park was quantitatively evaluated to diagnose pertinent issues. Additionally, optimization and reformation strategies were proposed. Therefore, the established model can achieve optimization of pollution reduction and operation costs for the entire industrial park, thus contributing to industrial wastewater pollution control and water quality improvement.Keywords: Industrial park; Monte Carlo model; Operation cost; Pollution reduction; Wastewater treatment
Year: 2018 PMID: 30077839 DOI: 10.1016/j.scitotenv.2018.07.358
Source DB: PubMed Journal: Sci Total Environ ISSN: 0048-9697 Impact factor: 7.963