Sensor validation and reconciliation for a partial nitrification process

Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1)

In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment process (WWTP) ensures increased efficiency and effluents meeting stringent emission norms. Benchmark simulation model No. 1 (BSM1) provides a simulation platform to researchers for developing efficient data-based process monitoring, quality monitoring, and process control systems for WWTPs. The present article presents a review of all research works reporting applications of various machine learning techniques for sensor and process fault detection of BSM1. The review focuses on process monitoring of biological wastewater treatment process, which uses a series of aerobic and anaerobic reactions followed by secondary settling process. Detailed information on various parameters monitored, different machine learning techniques explored, and results obtained by different researchers are presented in tabular and graphical format. In the review, it was observed that principal component analysis (PCA) and its variants account for the maximum number of research works for process monitoring in WWTPs and there are very few applications of recently developed deep learning techniques. Following the review and analysis, various future scopes of research (such as techniques yet to be explored or improvement of results for a particular fault) are also presented. These information will assist prospective researchers working on BSM1 to take forward the research.

Optimal flow sensor placement on wastewater treatment plants

Obtaining high quality data collected on wastewater treatment plants is gaining increasing attention in the wastewater engineering literature. Typical studies focus on recognition of faulty data with a given set of installed sensors on a wastewater treatment plant. Little attention is however given to how one can install sensors in such a way that fault detection and identification can be improved. In this work, we develop a method to obtain Pareto optimal sensor layouts in terms of cost, observability, and redundancy. Most importantly, the resulting method allows reducing the large set of possibilities to a minimal set of sensor layouts efficiently for any wastewater treatment plant on the basis of structural criteria only, with limited sensor information, and without prior data collection. In addition, the developed optimization scheme is fast. Practically important is that the number of sensors needed for both observability of all flows and redundancy of all flow sensors is only one more compared to the number of sensors needed for observability of all flows in the studied wastewater treatment plant configurations.

Assessment of environmental data quality and its effect on modelling error of full-scale plants with a closed-loop mass balancing

Environmental plants are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function, where the measurements and flow rate equipment in plants must be mutually consistent. The aim of this study is to detect any error in the measured data in an environmental plant and reconcile the data with some gross errors by using a closed data reconciliation of mass balance and the Lagrange multiplier method. A data reconciliation method based on closed-loop mass balance is suggested in order to reduce or remove error within data and obtain reliable process data. The proposed method is applied to a full-scale plant to detect the gross error in measured data, investigate the effects of erroneous data on modelling errors and compare the modelling performances of the faulty data and reconciled data. The results show that the proposed method can efficiently detect any gross error in data, estimate the error-free data by a reconciliation method and enhance the modelling accuracy by using reconciled data. This study provides a simple way to incorporate prior knowledge of plant modelling of a closed-loop mass balancing to identify any gross error and reconcile the faulty measurements.