A hybrid machine learning–based multi-objective supervisory control strategy of a full-scale wastewater treatment for cost-effective and sustainable operation under varying influent conditions

2021 ◽  
Vol 291 ◽  
pp. 125853 ◽  
Author(s):  
SungKu Heo ◽  
KiJeon Nam ◽  
Shahzeb Tariq ◽  
Juin Yau Lim ◽  
Junkyu Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Control Strategy ◽  
Supervisory Control ◽  
Cost Effective ◽  
Full Scale ◽  
Multi Objective ◽  
Sustainable Operation ◽  
Hybrid Machine

Supervisory control strategies for the new WWTP of Galindo-Bilbao: the long run from the conceptual design to the full-scale experimental validation

2006 ◽  
Vol 53 (4-5) ◽  
pp. 193-201 ◽  
Author(s):  
E. Ayesa ◽  
A. De la Sota ◽  
P. Grau ◽  
J.M. Sagarna ◽  
A. Salterain ◽  
...  
Keyword(s):  
Control Strategy ◽  
Supervisory Control ◽  
Experimental Validation ◽  
Scale Validation ◽  
Control Strategies ◽  
Treatment Plant ◽  
Average Concentration ◽  
Full Scale ◽  
Control Loop ◽  
Long Run

This paper presents the theoretical basis and the main results obtained during the development and full-scale experimental validation of the new supervisory control strategy designed for the Galindo-Bilbao wastewater treatment plant (WWTP). The different phases of the project have been carried out over the last 8 years, combining model simulations, pilot-plant experimentation and full-scale validation. The final control strategy combines three complementary control loops to optimise the nitrogen removal in pre-denitrifying activated sludge plants. The first controller was designed to maintain the average concentration of the ammonia in the effluent via the automatic selection of the most appropriate DO set point in the aerobic reactors. The second control loop optimises the use of the denitrification potential and finally, the third control loop maintains the selected amount of biomass in the biological reactors by automatic manipulation of the wastage rate. Mobile-averaged windows have been implemented to incorporate commonly used averaged values in the control objectives. The performance of the controllers has been successfully assessed through the full-scale experimental validation in one of the lines of the WWTP.

scholarly journals Multi-objective optimisation with hybrid machine learning strategy for complex catalytic processes

Energy and AI ◽  
2021 ◽  
pp. 100134
Author(s):  
Xin Yee Tai ◽  
Raffaella Ocone ◽  
Steven D.R. Christie ◽  
Jin Xuan
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Multi Objective ◽  
Hybrid Machine ◽  
Catalytic Processes

The A–B Process: A Novel two Stage Wastewater Treatment System

1985 ◽  
Vol 17 (2-3) ◽  
pp. 235-246 ◽  
Author(s):  
A. I. Versprille ◽  
B. Zuurveen ◽  
Th Stein
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Reduction Rate ◽  
Cost Effective ◽  
Full Scale ◽  
Treatment System ◽  
Organic Load ◽  
Wastewater Disposal ◽  
Cost Effective Alternative ◽  
Treatment Facilities

New acts on wastewater disposal demand for higher process stability and effluent quality. The A-B process, a novel two step treatment system, meets these requirements in a cost effective way. Five full-scale plants have been put in operation over the last two years. The objective of this paper is to give an outline of the features of the A-B system in the context of the results of these full-scale plants. In spite of the extreme high load, the A-stage can be operated at a high reduction rate and is stable. Variations in the organic load and pH- and toxic shocks are leveled out and a constant, mainly soluble effluent is supplied. This implicates a low sludge production in the B-stage. As a consequence higher overall reduction rates are obtained as compared to conventional processes at the same sludge load. Very low and stable final effluent concentrations are observed in all full-scale plants. Of special interest are the possibilities of upgrading existing conventional treatment facilities, at minor costs, by incorporating the A-B technology. The A-B process therefore can be considered as a very promising, cost effective alternative for both existing and new wastewater treatment plants in responding to the increasing effluent demands.

Functional Approach Leads to Successful ICA across 124 Water and 389 Wastewater Treatment Works

1993 ◽  
Vol 28 (11-12) ◽  
pp. 37-43
Author(s):  
M. J. Parker ◽  
R. J. Casey ◽  
L. K. Reynolds ◽  
R. R. de Vries ◽  
T. M. Brueck ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Process Control ◽  
Customer Service ◽  
Control Strategy ◽  
Cost Effective ◽  
Functional Approach ◽  
Director General ◽  
Functional Requirements ◽  
Systems Research ◽  
And Control

Thames Water Utilities Limited operates a complex network of 124 water and 389 wastewater treatment works. Regulatory requirements are driving a massive capital programme of more than ₤2 Billion over 5 years to improve performance of these facilities. Improvements are planned, engineered, and operated by a variety of internal and external groups and individuals. Shareholders are demanding profitability and the Director General is pushing for improvements to customer service. To achieve its objectives, Thames has developed an instrumentation, control and automation (ICA) systems master plan. The plan is based on the utility s water and wastewater functional requirements. The functional approach is applied to the large number of diverse facilities by grouping facilities and processes into classifications. The approach is used throughout the plan. Each process and its associated process control strategy are clearly defined to ensure consistency and the cost-effective implementation of instrumentation and control across Thames. The functional approach was developed for each process control strategy using a series of structured workshops to achieve consensus and company-wide acceptance. Each department of the utility was involved in the workshops (including engineering, operations, maintenance, management systems, research and development, operational science). Top management sponsored the planning effort and committed to making the plan a reality.

A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive phycobiliproteins production in Nostoc sp. CCC-403

2021 ◽  
Vol 329 ◽  
pp. 124908
Author(s):  
Dinesh Kumar Saini ◽  
Amit Rai ◽  
Alka Devi ◽  
Sunil Pabbi ◽  
Deepak Chhabra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Multi Objective ◽  
Machine Learning Approach ◽  
Hybrid Machine

scholarly journals Reconsideration and upgrading of sampling and analysis methods for avoiding measurement-related design and operation failures in wastewater treatment

Water SA ◽  
2019 ◽  
Vol 45 (3 July) ◽  
Author(s):  
V Bakos ◽  
A Deák ◽  
A Jobbágy
Keyword(s):  
Wastewater Treatment ◽  
Input Data ◽  
Wastewater Treatment Plants ◽  
Comparative Method ◽  
Cost Effective ◽  
Electrochemical Measurement ◽  
Full Scale ◽  
Floc Structure ◽  
Wastewater Quality

Success of design and high operational efficiency may basically stand or fall on the quality of measured (or estimated) input data. Even small mistakes committed in the initial steps of sampling and analysis may become large once scaled up in the design process or during full-scale operation. The paper provides several experiment-based practical recommendations and easily implemented, powerful methods for appropriate sampling and analysis practice in wastewater treatment. Representative wastewater characterization is crucial for satisfactory design and cost-effective operation. The paper highlights hidden problems and challenges of sampling and analysis in activated sludge wastewater treatment which may strongly affect the quality of input data, and thus basically determine the modelling outputs. Full-scale results proved that wastewater quality may change significantly in the sampling tubing and vessels; during the sampling process even nitrification can happen. Regarding sludge settling measurements, effects of dilution, temperature, floc structure, nitrate and dissolved oxygen concentrations as well as current biochemical condition of the sludge sample have been studied and important recommendations provided. A combined comparative method including SVI and DSVI measurements has been elaborated for indication and early warning alert of undesired floc structure transformations. Influent BOD5 concentration is a key factor for describing biodegradability and denitrification capacity of wastewater to be treated. Results of the two most commonly used BOD testing methods were compared for preclarified wastewater. An electrochemical measurement technique provided significantly lower BOD5 concentrations compared to manometric analysis results with a difference of 23% and 15% on average for unfiltered and filtered samples, respectively. Effects of BOD-based fractionation deviations on predictable denitrification efficiency were studied at different inlet C/N ratios by simulating existing full-scale wastewater treatment plants resulting in remarkable differences in effluent nitrate concentrations. Based on the results, application of the manometric BOD measurement method proved to be preferable.

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