Automating process design by coupling genetic algorithms with commercial simulators: a case study for hybrid MABR processes.

The development of commercial software and simulators has progressed to assist engineers to optimize design, operation, and control of wastewater treatment processes. Commonly, manual trial-and-error approaches combined with engineering experience or exhaustive searches are used to find candidate solutions. These approaches are becoming less favorable because of the increasingly elaborate process models, especially for new and innovative processes whose process knowledge is not fully established. This study coupled genetic algorithms (GAs), a subfield of artificial intelligence (AI), with a commercial simulator (SUMO) to automatically complete a design task. The design objective was the upgrade of a conventional Modified Ludzack-Ettinger (MLE) process to a hybrid membrane aerated biofilm reactor (hybrid MABR). Results demonstrated that GAs can (1) accurately estimate five influent wastewater fractions using eleven typical measurements - 3 out of 5 estimated fractions were nearly the same and the other two were within 7% relative errors and (2) propose reasonable designs for the hybrid MABR process that reduce footprint by 17%, aeration by 57%, and pumping by 57% with significantly improved effluent nitrogen quality (TN<3 mg-N/L). This study demonstrated that tools from AI promote efficiency in wastewater treatment process design, optimization and control by searching candidate solutions both smartly and automatically in replacement of manual trial-and-error methods. The methodology in this study contributes to accumulating process knowledge, understanding trade-offs between decisions, and finally accelerates the learning pace for new processes.

An adaptive real-time grey-box model for advanced control and operations in WRRFs.

Grey-box models, which combine the explanatory power of first-principle models with the ability to detect subtle patterns from data, are gaining increasing attention in wastewater sectors. Intuitive, simple structured but fit-for-purpose grey-box models that capture time-varying dynamics by adaptively estimating parameters are desired for process optimization and control. As an example, this study presents the identification of such a grey-box model structure and its further use by an extended Kalman filter (EKF), for the estimation of the nitrification capacity and ammonia concentrations of a typical Modified Ludzack-Ettinger (MLE) process. The EKF was implemented and evaluated in real time by interfacing Python with SUMO (Dynamita™), a widely used commercial process simulator. The EKF was able to accurately estimate the ammonia concentrations in multiple tanks when given only the concentration in one of them. In addition, the nitrification capacity of the system could be tracked in real time by the EKF, which provides intuitive information for facility managers and operators to monitor and operate the system. Finally, the realization of EKF is critical to the development of future advance control, for instance, model predictive control.

Digital solutions for continued operation of WRRFs during pandemics and other interruptions.

This paper includes survey results from 17 full-scale water resource recovery facilities (WRRFs) to explore their technical, operational, maintenance, and management-related challenges during COVID-19. Based on the survey results, limited monitoring and maintenance of instrumentation and sensors are among the critical factors during the pandemic which resulted in poor data quality in several WRRFs. Due to lockdown of cities and countries, most of the facilities observed interruptions of chemical supply frequency which impacted the treatment process involving chemical additions. Some plants observed influent flow reduction and illicit discharges from industrial wastewater which eventually affected the biological treatment processes. Delays in equipment maintenance also increased the operational and maintenance cost. Most of the plants reported that new set of personnel management rules during pandemic created difficulties in scheduling operator's shifts which directly hampered the plant operations. All the plant operators mentioned that automation, instrumentation, and sensor applications could help plant operations more efficiently while working remotely during pandemic. To handle emergency circumstances including pandemic, this paper also highlights resources and critical factors for emergency responses, preparedness, resiliency, and mitigation that can be adopted by WRRFs.

Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors.

Increased availability and affordability of sensors, especially water quality sensors, is poised to improve process control and modelling in water and wastewater systems. Sensor measurements are often flawed by unavoidable influent complexity and sensor instability, making extraction of useful signals problematic. Although a natural reaction is to put extra effort into sensor maintenance to achieve more reliable measurements, useful signals can be extracted from those unqualified signals by appropriate usage of available data-driven tools instructed by physical factors (e.g. prior process knowledge, physical constraints, phenomenal observations). Such methodology is herein defined as hybrid approaches. While the concept of coupling physical factors into data-driven tools is not new in downstream applications such as process modelling and control, little literature has explicitly applied it in the first and equally important step - signal processing. With flawed influent five-day biochemical oxygen demand (BOD5) sensor measurements as an example, this paper provides a comprehensive case study demonstrating how physical factors were incorporated throughout the procedures of processing a flawed signal for its maximum value. Results showed that useful signals were extracted and validated via an assembly of well-established machine learning tools, whose performance was improved with physical factors. An Improved Standard Signal Processing Architecture (ISSPA) is also proposed based on the results of this research.

Optimizing the configuration of integrated nutrient and energy recovery treatment trains: A new application of global sensitivity analysis to the generic nutrient recovery model (NRM) library.

This paper describes the use of global sensitivity analysis (GSA) for factor prioritization in nutrient recovery model (NRM) applications. The aim was to select the most important factors influencing important NRM model outputs such as biogas production, digestate composition and pH, ammonium sulfate recovery, struvite production, product purity, particle size and density, air and chemical requirements, scaling potential, among others. Factors considered for GSA involve: 1) input waste stream characteristics, 2) process operational factors, and 3) kinetic parameters incorporated in the NRMs. Linear regression analyses on Monte Carlo simulation outputs were performed, and the impact of the standardized regression coefficients on major performance indicators was evaluated. Finally, based on the results, the paper describes the original use of GSA to obtain insight in complex nutrient recovery systems and to propose an optimal nutrient and energy recovery treatment train configuration that maximizes resource recovery and minimizes energy and chemical requirements.