Soft Sensing Membrane Distillation Dynamic Model
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Solution Overview
Problem
Conventional desalination methods face challenges in efficiency and environmental impact, particularly with membrane distillation (MD) processes that lack dynamic modeling to accommodate intermittent renewable energy sources like solar energy, leading to inefficiencies and operational complexities.
Innovation Solution
A dynamic mathematical model based on the two-dimensional Advection-Diffusion Equation (ADE) is developed to estimate membrane mass transfer coefficients and predict heat and mass transfer mechanisms in membrane distillation (MD) processes, enabling real-time control and optimization, especially when powered by intermittent energy sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If membrane distillation processes are powered by intermittent renewable energy sources like solar energy, then environmental sustainability is improved, but operational efficiency and control stability deteriorate due to lack of dynamic modeling
Solution Approach 1:
The patent implements dynamic modeling of membrane distillation processes that can adapt to varying energy input conditions from intermittent renewable sources. The system uses real-time parameter estimation and dynamic control strategies to maintain optimal operation despite fluctuations in solar or other renewable energy supply, thereby preserving operational efficiency while utilizing sustainable energy sources.
Solution Approach 2:
The system dynamically adjusts operational parameters such as temperature, flow rates, and pressure based on real-time conditions and energy availability. By continuously monitoring and adapting parameters, the system maintains high productivity even when powered by intermittent renewable energy sources that cause varying operating conditions.
2Productivity
If dynamic modeling is implemented to accommodate intermittent energy sources, then operational control and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent replaces complex physical measurement systems with soft sensing and mathematical modeling approaches. Instead of installing numerous physical sensors throughout the system, the invention uses computational models and parameter estimation techniques to infer system states, thereby reducing hardware complexity while maintaining control capability.
Solution Approach 2:
The system introduces mathematical models and parameter estimation algorithms as intermediaries between the physical process and control actions. These computational intermediaries translate complex dynamic behavior into manageable control parameters, simplifying the overall control architecture while enabling efficient operation under intermittent energy supply.
3Measurement precision
If soft sensing and parameter estimation are used to monitor MD processes, then control precision is improved, but measurement and detection difficulty increases
Solution Approach 1:
The patent creates computational copies or representations of difficult-to-measure internal parameters through soft sensing and parameter estimation. Instead of directly measuring internal temperatures, concentrations, or fluxes that are inaccessible or difficult to detect, the system uses mathematical models to generate accurate estimates of these parameters based on readily available measurements, thereby achieving high measurement precision without direct physical measurement.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model enhances the productivity and energy efficiency of MD processes, allowing for better automation and control, and accurately predicts both steady-state and transient phases, making it suitable for integration with renewable energy sources.
Implementation Method 1
A dynamic mathematical model based on the two-dimensional Advection-Diffusion Equation (ADE) is developed to estimate membrane mass transfer coefficients and predict heat and mass transfer mechanisms
Implementation Method 2
A dynamic mathematical model based on the two-dimensional Advection-Diffusion Equation (ADE) is developed to estimate membrane mass transfer coefficients and predict heat and mass transfer mechanisms
Implementation Method 3
The separation is achieved under the validation of vapor-liquid equilibrium principle for molecules separation
Implementation Method 4
These properties allow the transfer of only water vapor or other volatile molecules through the membrane dry pores
Data Source
AI summary
Various examples of methods and systems are provided for soft sensing of system parameters in membrane distillation (MD). In one example, a system includes a MD module comprising a feed side and a permeate side separated by a membrane boundary layer; and processing circuitry configured to estimate feed solution temperatures and permeate solution temperatures of the MD module using monitored outlet temperatures of the feed side and the permeate side. In another example, a method includes monitoring outlet temperatures of a feed side and a permeate side of a MD module to determine a current feed outlet temperature and a current permeate outlet temperature; and determining a plurality of estimated temperature states of a membrane boundary layer separating the feed side and the permeate side of the MD module using the current feed outlet temperature and the current permeate outlet temperature.