The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

A vital process in wastewater treatment is the biological treatment with activated sludge. In Romania, there are still plenty of cases where the biological treatment stage is operated manually, leading to inefficient operation and implicitly to higher operational costs, increased difficulty in maintaining the quality of the effluent and less disturbance rejection capabilities. In our paper, we show that using a medium-fidelity first principle model and by carrying out an RGA analysis, a decentralized control strategy using PI controllers can be easily developed and tested before implementation, the control strategy giving good results in disturbance rejection and trajectory tracking. Keywords-wastewater treatment; RGA; decentralized control978-1-4799-7800-7/15/$31.00 ©2015 IEEE

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