Wastewater treatment plant operations represent a multidisciplinary technical-scientific domain with significant implications for terrestrial and marine ecosystems, as well as public health considerations.
The inherent complexity of this field necessitates an interdisciplinary approach to treatment plant design and management, particularly regarding sustained treatment effectiveness, energy efficiency, and sludge reduction.
Knowledge-Based (KB) Approach
A Knowledge-Based approach relies on expert knowledge and cross-correlation algorithms governing numerous process parameters — both physicochemical and microbiological. This methodology enables operations with "maximum awareness" and strategic direction, ultimately achieving continuous high-quality effluent production alongside reduced sludge volumes.
Through KB algorithms utilizing input/output and process parameters, facilities can determine the Functional Perimeter for each treatment section and define its Residual Treatment Ability. Input/output data derives primarily from daily average measurements and hourly readings (influent flow, dissolved oxygen, etc.).
Data-Driven (DD) Approach
The Data-Driven approach relies on acquiring and analyzing measurement data (online/real-time) from wastewater treatment, sludge production, and energy consumption processes.
While hydraulic measurements (flows, volumes, levels) and electrical consumption (kW) are relatively straightforward to obtain, chemical and microbiological parameters (nutrient concentrations, MLSS biological sludge concentrations, microscopic analysis, SVI sludge index) require periodic laboratory measurements or indirect measurements (ORP, DO). Consequently, sustainable DD approaches maintain hourly or daily data acquisition frequencies — essentially matching KB methodology.
Machine Learning and Deep Learning systems require extensive, well-distributed multi-parametric databases, often unavailable and unsustainable for typical small-to-medium facilities.
Conclusion
A hybrid, modular KB/DD approach appears most effective for operational efficacy and achieving strategic sustainability and circular economy objectives.