Water Treatment Plant Imaging Control via Machine Learning
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Solution Overview
Problem
Conventional water treatment plants using AI devices for control struggle with effective water treatment control when environmental changes are not detected by numerical sensors, leading to suboptimal management.
Innovation Solution
Incorporating an imaging device to capture environmental data, a processing device to analyze this data using machine learning models, and a control device to adjust water treatment operations based on the analysis, enabling more responsive and effective control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional numerical sensors are used for water treatment control, then the control system is simple and easy to operate, but it cannot detect environmental changes that do not appear as numerical value changes
Solution Approach 1:
The patent combines multiple detection approaches by integrating image data from imaging devices with numerical data from sensors, and processes both through machine learning models to generate comprehensive control decisions. This merging allows the system to detect both visual environmental changes and numerical parameter changes, resolving the contradiction between detection capability and system complexity.
Solution Approach 2:
The patent introduces an image processing intermediary that converts visual environmental information into actionable control data through machine learning models. The imaging device captures visual changes, the processing device analyzes them using trained models, and the control device acts on the results, creating an intermediary detection pathway that complements direct numerical sensing.
2Adaptability or versatility
If operator experience is used to change control target values, then specialized expertise is required, but the system cannot adapt to new environmental conditions without retraining operators
Solution Approach 1:
The patent implements a self-learning control system where the machine learning models automatically adapt to new environmental conditions by processing image and sensor data. The system trains itself on historical data and continuously improves its control decisions without requiring operator retraining, enabling automatic adaptation to seasonal changes, equipment variations, and new operational scenarios while maintaining ease of operation.
Solution Approach 2:
The patent establishes a feedback loop where the control device receives continuous input from both imaging devices and sensors, processes this information through machine learning models, and adjusts control target values accordingly. This closed-loop feedback system automatically learns from past performance and environmental changes, replacing the need for operator experience while maintaining adaptive control.
3Reliability
If only sensor numerical values are used for control, then the control system is simple to implement, but effective water treatment control cannot be performed when environmental changes are not reflected in numerical values
Solution Approach 1:
The patent adds a visual dimension to the control system by incorporating imaging devices that capture spatial and qualitative environmental information not available through numerical sensors alone. This dimensional expansion allows detection of environmental changes such as color variations, turbidity, foam formation, and equipment states that do not manifest as numerical value changes, thereby improving control effectiveness while justifying the increased system complexity.
Solution Approach 2:
The patent transforms visual image data into actionable control parameters through machine learning processing. The system converts complex image information into simplified control target values that can be directly applied to water treatment operations, effectively changing the parameter representation from raw images to actionable numerical control signals, thus improving reliability without making the system overly complex.
Data Source
AI summary
A water treatment plant which performs water treatment using a water treatment device includes an imaging device, a processing device, and a control device. The imaging device images a water treatment environment of the water treatment device and outputs image data obtained by imaging. The processing device causes an arithmetic device which performs an arithmetic operation using one or more calculation models generated by machine learning to execute the arithmetic operation employing the image data output from the imaging device as input data of the one or more calculation models. The control device controls the water treatment device on the basis of output information output from the arithmetic device by executing the arithmetic operation.