AIoT Coagulation Parameter Optimization for Variable Water Quality
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Solution Overview
Problem
Existing water treatment methods struggle to adjust coagulation operating parameters in real time due to variable raw-water quality, leading to inefficiencies, increased energy and personnel costs, and unstable treatment outcomes, with previous research focusing primarily on coagulant dosage without considering stirring speed.
Innovation Solution
Utilizing algorithms like Back Propagation Neural Network, Extreme Learning Machines, and Multiple Nonlinear Regression to develop an optimized coagulation procedure that considers both coagulant dosage and stirring speed, leveraging AIoT for real-time adjustments based on water quality conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If coagulation operating parameters are adjusted based on subjective operator experience and historical data, then the operation is simple to perform, but the water purification efficiency is insufficient and treatment outcomes are unstable
Solution Approach 1:
The patent replaces the mechanical system of manual operator judgment and adjustment with an AI-based automated system. The neural network model automatically analyzes water quality parameters and recommends optimal coagulation operating parameters, substituting human subjective decision-making with objective algorithmic processing. This resolves the contradiction by maintaining ease of operation (the system is user-friendly) while dramatically improving reliability (consistent, data-driven decisions).
Solution Approach 2:
The system enables self-service by allowing the coagulation process to automatically adjust its own parameters based on real-time water quality monitoring. The AI model continuously learns from historical data and automatically optimizes coagulant dosage and mixing parameters without requiring external expert intervention, making the process self-regulating and improving both efficiency and stability.
2Reliability
If coagulation operating parameters are adjusted in real time according to different water conditions, then the water purification efficiency is improved, but the manpower burden is increased
Solution Approach 1:
The patent replaces manual real-time adjustment operations with an automated AI-based parameter recommendation system. The neural network model continuously processes water quality data and generates optimal operating parameter recommendations automatically, eliminating the need for operators to manually monitor and adjust parameters in real time. This resolves the contradiction by maintaining high water purification efficiency through continuous optimization while significantly reducing manpower burden.
3Device complexity
If only coagulant dosage is controlled without considering stirring speed, then the control process is simple, but the coagulation efficiency is not fully optimized
Solution Approach 1:
The patent merges the control of coagulant dosage and stirring speed into a unified AI-based optimization system. The neural network model simultaneously considers both parameters and their interactions to generate coordinated recommendations, recognizing that these parameters work together in the coagulation process. This resolves the contradiction by maintaining relatively simple implementation (a single integrated model) while achieving fully optimized coagulation efficiency through multi-parameter coordination.
Solution Approach 2:
The system dynamically adjusts multiple operating parameters (coagulant dosage, stirring speed, mixing time) based on real-time water quality conditions. The AI model identifies optimal combinations of these parameters rather than controlling them independently, allowing the system to adapt to varying water conditions and maximize coagulation efficiency while keeping the control framework manageable through automated parameter interrelation.
4Ease of manufacture
If historical data from operator subjective experiences is used for model training, then the model development is straightforward, but the operating parameters obtained are not optimal and costs are increased
Solution Approach 1:
The patent implements a feedback mechanism where the AI model is trained on comprehensive historical data including water quality parameters, operating parameters, and treatment outcomes. The system continuously learns from this feedback loop, adjusting its predictions to optimize both coagulant dosage and stirring speed. This resolves the contradiction by maintaining straightforward model development (using available historical data) while achieving optimal operating parameters through iterative learning and validation against actual treatment performance.
Data Source
AI summary
The present invention is to optimize recommended coagulation operating parameters for water treatment. The optimization is aimed to apply Artificial Intelligence of Things for optimizing coagulation procedure with energy saved and cost reduced. The optimizing of the coagulation procedure is that the training data, such as water quality conditions, coagulant dosages, mixing speeds, etc., are used to estimate corresponding turbidity changes; and, after considering the drinking water standards and the cost of drug and energy, optimized operating parameters are found. The result shows that the accuracy of multiple nonlinear regression is high, where the root mean square difference for turbidity is 1.57 nephelometric turbidity units. In overall, the optimized coagulation procedure accurately estimates coagulation-related operating parameters based on changes in water quality.