Environmental Science and Pollution Research

, Volume 23, Issue 24, pp 25451–25466 | Cite as

Development of a protocol to optimize electric power consumption and life cycle environmental impacts for operation of wastewater treatment plant

  • Wenhua Piao
  • Changwon Kim
  • Sunja Cho
  • Hyosoo Kim
  • Minsoo Kim
  • Yejin Kim
Research Article
First Online:
Received:
Accepted:
  • 174 Downloads

Abstract

In wastewater treatment plants (WWTPs), the portion of operating costs related to electric power consumption is increasing. If the electric power consumption decreased, however, it would be difficult to comply with the effluent water quality requirements. A protocol was proposed to minimize the environmental impacts as well as to optimize the electric power consumption under the conditions needed to meet the effluent water quality standards in this study. This protocol was comprised of six phases of procedure and was tested using operating data from S-WWTP to prove its applicability. The 11 major operating variables were categorized into three groups using principal component analysis and K-mean cluster analysis. Life cycle assessment (LCA) was conducted for each group to deduce the optimal operating conditions for each operating state. Then, employing mathematical modeling, six improvement plans to reduce electric power consumption were deduced. The electric power consumptions for suggested plans were estimated using an artificial neural network. This was followed by a second round of LCA conducted on the plans. As a result, a set of optimized improvement plans were derived for each group that were able to optimize the electric power consumption and life cycle environmental impact, at the same time. Based on these test results, the WWTP operating management protocol presented in this study is deemed able to suggest optimal operating conditions under which power consumption can be optimized with minimal life cycle environmental impact, while allowing the plant to meet water quality requirements.

Keywords

Wastewater treatment plant Electric power consumption Multivariate statistical analysis Mathematical modeling Life cycle assessment 

Nomenclature

A2/O

Anaerobic/anoxic/oxic process

PAC

Poly-aluminum chloride (kg/day)

SRT

Solids retention time (day)

HRT

Hydraulic retention time (day)

F/M ratio

Food to microorganism ratio

MLSS

Mixed liquor suspended solids (mg/l)

BOD

Biochemical oxygen demand (mg/l)

TN

Total nitrogen (mg/l)

TP

Total phosphorus (mg/l)

PCA

Principal component analysis

KMCA

k-mean clustering analysis

ANN

Artificial neural network

Air

Air flow rate (m3/day)

Qin

Influent flow rate (m3/day)

Qras

Return sludge flow rate (m3/day)

Qrasin

Inner return sludge flow rate (m3/day)

Qfir

Waste sludge flow rate in primary settling tank (m3/day)

Qsec

Waste sludge flow rate in secondary settling tank (m3/day)

Cake

Dewatered sludge cake production (t/day)

Qdi

Influent flow rate of digester (m3/day)

Qdw

Influent flow rate of dewatering facility (m3/day)

Qthic

Influent flow rate of centrifugal thickener (m3/day)

Elec

Electric power (kWh/day)

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Notes

Acknowledgments

This research was supported by the Korean Ministry of Environment and the Korean Environmental Industry and Technology Institute (KEITI) through the “The Eco-Innovation Project”. This work was also supported by the Brain Korea 21 Plus Project in the Division of Creative Low Impact Development and Management for Ocean Port City Infrastructures.

Supplementary material

11356_2016_7771_MOESM1_ESM.xlsx (37 kb)
ESM 1 (XLSX 37 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Wenhua Piao
    • 1
  • Changwon Kim
    • 2
    • 3
  • Sunja Cho
    • 4
  • Hyosoo Kim
    • 3
  • Minsoo Kim
    • 5
  • Yejin Kim
    • 6
  1. 1.Department of Environmental EngineeringPusan National UniversityBusanRepublic of Korea
  2. 2.Institute for Environmental Technology and IndustryPusan National UniversityBusanRepublic of Korea
  3. 3.EnvironSoft Inc., Ltd.YangsanRepublic of Korea
  4. 4.Department of MicrobiologyPusan National UniversityBusanRepublic of Korea
  5. 5.Department of Energy and Environmental System EngineeringUniversity of SeoulSeoulRepublic of Korea
  6. 6.Department of Environmental Engineering, School of Applied ScienceCatholic University of PusanBusanRepublic of Korea

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