Soft-Sensor pH Prediction for Seawater Treatment
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Solution Overview
Problem
Conventional pH measurement techniques in seawater processing plants are costly and require frequent maintenance and calibration, leading to potential inaccuracies and operational issues if not regularly checked, which can result in severe consequences such as scale formation and increased bacterial growth.
Innovation Solution
Implementing a computer-implemented method using soft-sensors that predict pH levels in seawater based on process parameter values and historical data, eliminating the need for physical sensors and regular calibration, and utilizing neural network software to develop a model that accurately predicts pH values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical pH sensors are installed in the field to measure pH, then pH measurement capability is achieved, but capital costs and maintenance requirements increase significantly
Solution Approach 1:
The patent replaces physical pH sensors with a soft-sensor system that uses neural network software and process parameter data to predict pH values. This substitutes mechanical/physical measurement devices with a computational model that processes existing process data (temperature, pressure, flow rates, chemical dosing rates) to estimate pH without requiring physical sensor installation.
Solution Approach 2:
The patent creates a virtual copy of the pH measurement function through a neural network model. Instead of using a physical sensor to directly measure pH, the system creates a computational replica that predicts pH based on correlations with other measurable process parameters, effectively copying the measurement capability through software rather than hardware.
2Reliability
If physical pH analyzers are used to monitor pH, then continuous pH monitoring is achieved, but regular calibration and maintenance are required to maintain accuracy
Solution Approach 1:
The soft-sensor system is self-servicing in that it continuously predicts pH using existing process data without requiring external calibration activities. The neural network model automatically processes incoming process parameters (temperature, pressure, flow rates, chemical dosing) to generate pH estimates, eliminating the need for manual calibration interventions that physical sensors require.
Solution Approach 2:
The patent enables continuous pH monitoring through the soft-sensor system that operates without interruption for calibration. Unlike physical analyzers that require periodic shutdowns for calibration, the neural network model continuously processes process data and generates pH predictions, maintaining uninterrupted monitoring of the desalination process.
3Measurement precision
If physical pH sensors are installed at multiple treatment modules, then comprehensive pH coverage is achieved, but capital costs increase proportionally
Solution Approach 1:
The soft-sensor system serves multiple treatment modules simultaneously using a single neural network model. Instead of installing separate physical sensors at each of the 28 treatment modules, the patent deploys one universal soft-sensor system that processes plant-wide process data to predict pH across multiple modules, making the measurement system multi-functional and scalable.
Solution Approach 2:
The patent merges the pH measurement function across multiple treatment modules into a single integrated soft-sensor system. By combining process data from various modules and using a unified neural network model, the system consolidates what would require multiple separate physical sensors into one computational platform, reducing the total number of measurement devices needed.
Data Source
AI summary
Systems and methods include a computer-implemented method for predicting pH of seawater. A model is generated that is configured to predict a power of hydrogen (pH) of treated seawater. Generating the model includes correlating process parameter values and historical data of seawater processing plants of oil and gas reservoirs. Upstream parameters of the seawater plant are received by a soft sensor pH predictor installed at a seawater plant. A pH of seawater being processed by the seawater plant is predicted using the model and neural network software of the soft sensor pH predictor.