Weather-based prediction strategy inside the proactive historian with application in wastewater treatment plants

Abstract

The current landscape in the water industry is dominated by legacy technical systems that are inefficient and unoptimized. In recent years, sustained efforts could be identified, especially under the guidance of the Industrial Internet of Things (IIoT) paradigm, in order to develop an increased level of both connectivity and intelligence in the functioning of industrial processes. This led to the emergence of the data accumulation concept, materialized in the practical sphere by Historian applications. Although various classic Historian solutions are available, the capability to optimize and influence the monitored system in a proactive way, resulting in increased efficiency, cost reduction, or quality indicators improvements, could not be identified to date. Following a proposed software reference architecture for such a proactive Historian, a data dependency identification strategy and some obtained recipes for energy efficiency improvements in the water industry were developed. However, a complete solution for real industrial processes represents complex research. The current paper contributes to this research effort by developing part of the reference architecture that predicts the future evolution of the monitored system, based on weather dependency and forecast, thus sustaining the effort to achieve a fully functional, real-world, tested and validated proactive Historian application, with potential to bring significant direct benefits to the water industry.