Modelling SBR cycle management and optimization using events and workflows

Sequencing Batch Reactors (SBRs) provide several advantages in terms of flexibility and robustness to variations in the inflow concentrations and sludge alterations. Adjusting the duration of the load, reaction and discharge phases, it allows to optimize the treatment, saving time and energy. Optimal policies can be defined by observing and analyzing some chemical and physical parameters such as pH, redox potential and dissolved oxygen concentration. Various Artificial Intelligence (AI) techniques have been proposed and used to recognize the state of the biological processes inside the plant, using the signals' trends and changes as indirect indicators. In particular, the termination of a process (typically, denitrification in the anoxic phase or nitrification in the aerobic one) can be estimated by a management system and used in control policies. In this paper, we point out that this recognition task is only part of the responsibilities of a potential Environmental Decision Support System (EDSS) managing an SBR, and by no means the only one which can take advantage of AI techniques. In fact, the entire control and management system could be defined and implemented using declarative AI techniques, deployed within a uniform execution environment. In particular, we propose two alternative but similar models of the SBR operation cycles: one is based on workflows, exploiting the BPMN2 (Business Process Management Notation v.2) standard for the definition and execution of business processes; the other instead is founded on the principles of (Reactive) Event Calculus. Both representation capture the operational behavior of the SBR, supporting the state transitions and the actions associated to each state. The transitions themselves are driven by events, i.e. relevant state changes. The events are identified either directly, through the sensor system installed on the plant, or analysing a combination of other more elementary events, and conditioned by the actual state of the plant. The correlations between events, states and control actions, as well as their consequences, will eventually be defined using rules, which will also encode the necessary knowledge to deal with exceptional conditions, accounting for a more flexible system.