A Decision Support Tool for Screening Novel WWT Processes

Abstract

Wastewater is a by-product of human activities (domestic, industrial, commercial, agricul­tural, etc.) and should be treated properly before discharge to a water body or reuse. In the 21st century, as we have started to acknowledge that wastewater is a renewable resource from which water, energy, and materials can be recovered, the demand for building new resource recovery facilities or retrofitting of existing wastewater treatment plants (WWTPs) has gained increased interest among various stakeholders (such as utility managers, opera­tors, regulators, end-users, etc.). The primary problem in deploying a sustainable solution is not the lack of availability of suitable resource recovery technologies, but rather the lack of a systematic planning and design methodology. Since the inception of the activated sludge process, the horizon of innovative wastewater treatment solutions keeps on increasing and so are the priorities. Several times the priorities set by the stakeholders are self-conflicting, which further narrows down the flexibility of decision-makers. Therefore screening and de­signing wastewater treatment technologies for building/retrofitting a WWTP is a formidable challenge. To address the above problem with more systematic approaches, a new process synthesis tool (called SPDLab) is developed relying on a simulation/optimization-based process synthesis methodology and a library of high fidelity process models including both conventional and emerging technologies. This tool aims to combine several new features: (1) first-principles mechanistic models to be used to simulate alternative technologies, unlike the state-of-the­art. This key innovation ensures predictive capability, 2) the Monte Carlo technique and sim­ulation-based optimization techniques for handling effectively the resulting computational complexity, 3) a systematic initialization algorithm developed for plant-wide simulation, 4) optimizing design parameters, operational conditions, and plant layout simultaneously. To find an optimal solution, several realistic constraints are imposed, and multiple objectives are formulated and solved under both deterministic (i.e., no data uncertainty) and stochastic (i.e., data uncertainty) conditions. The purpose of the SPDLab tool development is to support the plant designers/decision-makers with providing a comprehensive report comparing dif­ferent plant layout alternatives. Moreover, the tool aims to be more user-friendly, where the end-user can reprioritize their objectives by continually learning from the outcomes. Finally, the application of the SPDLab tool is demonstrated through two case studies consti­tuting typical examples for designing a new wastewater treatment plant (for the BSM2 influ­ent, 100,000 PE) and retrofitting existing treatment plants (Valladolid WWTP of 1,067,033 PE, Lynetten WWTP of 750,000 PE and Aved0re WWTP of 265,000 PE) by prioritising sus­tainability, economics and energy resource recovery objectives.