Applying performance indices in plantwide modelling for a comparative study of wastewater treatment plant operational strategies

Abstract

Achievement of good effluent quality is always the main goal for wastewater treatment plant (WWTP) systems. However, these WWTPs have developed further objectives that include efficient design and strategic control options, with the prospect of their conversion into waste resource recovery facilities (WRRFs) that operate on reduced energy costs. With all these aspects becoming an intrinsic part of waste treatment, mathematical models that simulate WWTP unit processes are becoming of increasing relevance for the achievement of WRRF goals (including good effluent quality, low energy costs and nutrient recovery). It is expected that these mathematical models will benefit potential future applications of automation process control, which have also been developing rapidly with the availability of more reliable and affordable sensors. However, simulated automation control strategies require a thorough evaluation protocol to ensure their viability prior to being adopted as efficient operation control measures. This study considers the comparison of different control strategies implemented on a standard WWTP layout, for plant optimization. The initial task was to define performance indices, effluent quality index (EQI) and operation cost index (OCI), based on a previous investigation by the International Water Association (IWA) benchmark simulation modelling (BSM) task group. These performance indices were then used to evaluate the following strategies: (i) adding a fermentation tank, (ii) dosing flocculant and (iii) implementing a balancing tank. A control strategy was only assumed to be effective with improvement or maintenance of effluent quality. Overall, the evaluation exercise proved to be useful for providing expert advice on efficiency of proposed waste treatment system layouts, towards determination of the best configuration of future WRRFs. For instance, it was notable that significant organic strength is needed for removal of nutrients recycled back from the anaerobic digestion (AD) system into the activated sludge (AS) – hence alternate methods to put the nutrient-rich outflow from the AD system to good use are required.

Enhancing Textile Wastewater Treatment Performance: Optimization and Troubleshooting (Decision Support) via GPS-X Model

“…The study by Bhave et al [9] showed that periodic plant monitoring at a specific location ensures that the operator checks the performance of each unit process. The key locations where the plant is monitored are at influent, clarifiers, aeration tank, internal recycles, and effluent [10,11]. Similarly, the key parameters at the specific monitoring location that should be measured and controlled are sludge retention time (SRT), food-to-microorganism ratio (F/M), sludge volume index (SVI), air flow, recycle-activated sludge (RAS), and waste-activated sludge (WAS) [12][13][14][15].…”

“…The comprehensive optimization of process control parameters is indispensable to enhance the performance of a plant within optimum costs and energy [11]. Optimization requires multiple attributes integrated together to find out the best solution to save time, and increase decision capacity and quality [16,17].…”

Sustainable Energy Management Benchmark at Wastewater Treatment Plant

“…However, the direct relation between energy and WWT quality is absent. De Ketele et al [19], as well as the team considered above, analyze WWTP operation strategies in terms of transition towards Waste Resource Recovery Facilities using BSM under performance indexes-effluent quality index (EQI) and operation cost index (OCI). There is also no direct connection between quality and energy issues within this paper.…”

The impact of sludge return liquors on South African wastewater treatment plants

“…The result is the number of pollutants (in terms of kg) discharged per day. The EQI and OCI formulations provided by DeKetele et al (2018), based on the previous work by the IWA BSM task group(Jeppsson et al, 2007), are shown in Eq. 1 and Eq.…”