Big Data Analytics and Machine Learning Framework for Optimizing Struvite Precipitation in Smart Wastewater Treatment Plants: A Decision Support System Approach

Abstract

The paper presents a novel, information-based approach using machine learning and big data analytics to support struvite precipitation in the context of wastewater treatment plants. The framework is based on three interdependent functional stacks: a data ingestion stack that accounts for process instrumentation, process parameters, and surrounding environment monitors that feed data into a common integrated representation of the system; an analytics stack that employs ML algorithms to establish nonlinear mappings between parameters and predict healthy operating condition rods; and finally, a decision support interface that translates the analytics insights into actionable operating guidance.

The data integration layer (at the bottom of the above) uses strong validation protocols to syncronize multiple streams of reliable data. The analytics layer not only grants predictive performance by creating machine learning models, the machine learning creates adaptive behavior across the system, enabling real-time functionality without compromising its predictive performance. These recommendations at the decision support interface level, on the other hand, are based on all this complexity, distilled into actionable insights, aligned with operational transparency and flexible control of the same.

The architecture then proceeds to discuss high-level implementation considerations including infrastructure, operator training needs and system security procedures. Its modular structure offers flexibility, enables the relevance with existing operational constructs while also creating a pathway to innovations that will ultimately be the ‘tomorrow’ of the industry. By improving knowledge of reactor-associated microbial communities and dynamic processes, this guide aims to help achievement of increased phosphorus recovery efficiencies and further development of smart waste water treatment technologies.