Data-Knowledge-Driven Control for Wastewater Energy and Quality
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Solution Overview
Problem
The wastewater treatment process is complex and nonlinear, making it difficult to optimize energy consumption and effluent water quality simultaneously, as these are conflicting and coupled objectives, and the limited data collection hinders effective optimization and control.
Innovation Solution
A data-knowledge-driven optimization method is established using a multi-objective particle swarm optimization algorithm and PID control to optimize dissolved oxygen and nitrate nitrogen concentrations, based on a data-driven energy consumption and effluent water quality model, which reduces energy consumption and improves effluent water quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the wastewater treatment process focuses on improving effluent water quality through extensive data collection and complex mechanism modeling, then the water quality meets discharge standards, but the energy consumption increases and the control complexity increases due to the nonlinear and strongly coupled characteristics of the process
Solution Approach 1:
The patent transforms the complex mechanism model into a data-driven model that directly maps process parameters to effluent quality and energy consumption. By using input variables (nitrate nitrogen, dissolved oxygen, suspended solids, ammonia nitrogen) and applying radial basis kernel functions, the system learns optimal parameter relationships from historical data, avoiding the need for complex mechanistic understanding while achieving accurate prediction and optimization of both water quality and energy consumption
Solution Approach 2:
The patent introduces a multi-objective particle swarm optimization algorithm as an intermediary between the data-driven model and the control system. This optimization algorithm processes the model predictions and generates optimized setpoints for dissolved oxygen and nitrate nitrogen concentrations, mediating the conflict between water quality improvement and energy consumption reduction by finding optimal compromise solutions
2Measurement precision
If the wastewater treatment process collects extensive data over long periods to improve optimization performance, then the model accuracy improves, but the time required for data collection increases and the system response becomes slower
Solution Approach 1:
The patent performs preliminary data processing and model training offline using historical data, building a trained data-driven model before real-time control is needed. The radial basis kernel functions and optimization algorithms are pre-configured based on historical patterns, allowing the system to make rapid real-time predictions and adjustments without performing extensive computations during critical control periods
Solution Approach 2:
The patent replaces the traditional mechanism-based model with a data-driven model that uses statistical learning instead of physical laws. This substitution allows the system to capture complex nonlinear relationships in wastewater treatment without requiring detailed mechanistic understanding or extensive real-time data collection, achieving accurate predictions with less data while maintaining fast response times
3Loss of information
If the wastewater treatment process uses complex mechanism models to describe the biochemical reactions, then the theoretical understanding improves, but the difficulty of determining and implementing the control strategy increases due to different sewage treatment plants and their environments
Solution Approach 1:
The patent inverts the traditional approach by not starting with mechanism models and then trying to control them, but instead starting with observed input-output relationships and working backwards to derive control strategies. The data-driven model learns directly from historical data what parameter combinations lead to desired outcomes, eliminating the need to explicitly model complex biochemical reactions while still achieving effective control
Solution Approach 2:
The patent creates a universal data-driven modeling framework that can be applied to different wastewater treatment plants regardless of their specific configurations or environmental conditions. The radial basis kernel function approach and particle swarm optimization algorithm provide a general methodology that adapts to various plant types and conditions without requiring plant-specific mechanism models, making the control system universally applicable
Data Source
AI summary
A data-knowledge driven multi-objective optimal control method for municipal wastewater treatment process belongs to the field of wastewater treatment. To balance the energy consumption and effluent quality, a data driven multi-objective optimization model, including energy consumption model and effluent quality model are established to obtain the nonlinear relationship along energy consumption, effluent quality and manipulated variables. Meanwhile, a multi-objective particle swarm optimization algorithm, based on evolutionary knowledge, is proposed to optimize the set-points of nitrate nitrogen and dissolved oxygen. Moreover, the proportional integral differential (PID) controller is designed to track the set-points. Then the effluent quality can be improved and the energy consumption can be reduced.