4.7 Article

A hierarchical intelligent control strategy for greenhouse gas reduction in wastewater treatment process of paper mill

期刊

JOURNAL OF CLEANER PRODUCTION
卷 379, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2022.134818

关键词

Wastewater treatment; Greenhouse gas; Dissolved oxygen; Optimization; Control; Paper mill

资金

  1. Joint Research for INTERNATIONAL COOPERATION on Scientific and Technological Innovation by MOST [2021A1515010327]
  2. National Natural Science Foundation of Guangdong Province, China [52000078]
  3. National Natural Science Foundation of China [T7?KI-00426]
  4. European Union
  5. Greek national funds through the Operational Program ?
  6. Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation
  7. [2017YFE0184900]

向作者/读者索取更多资源

Due to the high pollution loads in the papermaking industry, a large amount of greenhouse gases are emitted during the wastewater treatment process. In order to reduce these emissions, an intelligent control scheme based on dissolved oxygen control has been developed. The simulation results show that the proposed hierarchical optimal proportional-integral control scheme can effectively reduce the greenhouse gas emissions compared to the open-loop operation.
Due to the huge amounts of wastewater discharge and the high pollution loads in papermaking industry, many greenhouse gases (GHG) are emitted in the papermaking wastewater treatment process. The wastewater dis-solved oxygen (DO) control has been considered as the most cost-effective control solution for GHG reduction in wastewater treatment plants (WWTP). However, the competition between contaminant removal efficiency and GHG reduction hinders the drastic reduction of GHG emissions from WWTP. In this study, based on the estab-lished integrated GHG emission model, explicitly considering the total GHG reduction targets on the premise of effluent compliance, an intelligent control scheme has been developed for an activated sludge process in a paper mill. Regarding DO as the controlled variable, the proposed hierarchical optimal proportional-integral (HOPI) control scheme was established consisting of three layers: 1) Layer 1 for the effluent quality estimation, 2) Layer 2 for the optimal DO set point determined by genetic algorithm with the influent variations to obey discharging norms and reduce GHG emissions, 3) Layer 3 for the DO tracking proportional integral (PI) control with the controller parameters adjusted by the back propagation neural network to track the dynamically optimized DO set points. The simulation results showed that, compared with the open-loop (OL) operation (averaged aeration, 10/h), the proposed HOPI control (averaged aeration, 7.9/h) reduced the GHG emissions by 12.54% under the premise of discharging norms, which was mainly attributed to the reduction of the aeration electricity con-sumption. In contrast, the PI control (averaged aeration, 12.9/h) increased the GHG emissions by 7.48% compared with the OL operation. Thus, the proposed HOPI control strategy has demonstrated potential for the application of GHG reduction in industrial WWTPs.

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