Deep learning in wastewater treatment: a critical review.

Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms, a high degree of disturbance variability and non-linear (generally stable) behavior with multiple internal recycle loops. Semi-mechanistic biochemical models currently dominate research and application, with data-driven deep learning models emerging as an alternative and supplementary approach. But these modeling approaches have grown in separate communities of research and practice, and so there is limited appreciation of the strengths, weaknesses, contrasts and similarities between the methods. This review addresses that gap by providing a detailed guide to deep learning methods and their application to wastewater process modeling. The review is aimed at wastewater modeling experts who are familiar with established mechanistic modeling approach, and are curious about the opportunities and challenges afforded by deep learning methods. We conclude with a discussion and needs analysis on the value of different ways of modeling wastewater processes and open research problems.

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems.

Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers' privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost-benefit perspective while ensuring data privacy.

Sweating the assets - The role of instrumentation, control and automation in urban water systems.

Instrumentation, control and automation (ICA) are currently applied throughout the urban water system at water treatment plants, in water distribution networks, in sewer networks, and at wastewater treatment plants. However, researchers and practitioners specialising in respective urban water sub-systems do not frequently interact, and in most cases to date the application of ICA has been achieved in silo. Here, we review start-of-the-art ICA throughout these sub-systems, and discuss the benefits achieved in terms of performance improvement, cost reduction, and more importantly, the enhanced capacity of the existing infrastructure to cope with increased service demand caused by population growth and continued urbanisation. We emphasise the importance of integrated control within each of the sub-systems, and also across the entire urban water system. System-wide ICA will have increasing importance with the growing complexity of the urban water environment in cities of the future.