Chemical Plant Precipitation Control With CFD and White-Box Models
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Solution Overview
Problem
Current methods for simulating and controlling technical precipitation processes in the chemical industry, such as those for pigments, catalysts, and crop science materials, are inadequate as they fail to provide model-based conception and design of precipitation assets and do not effectively understand the coherence between process and product morphology or phase composition, especially when scaling from lab to real processes.
Innovation Solution
A computer-implemented method that combines a physico-chemical white box model with computational fluid dynamics (CFD) and thermodynamics to optimize operating conditions and plant equipment layout, allowing for precise prediction and adjustment of parameters like particle size and supersaturation, thereby enabling better process control and design of chemical plant equipment layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If classical simulation approaches (thermodynamics, solid state formation, CFD) are used, then computational analysis is provided, but model-based conception and design of precipitation assets is not enabled
Solution Approach 1:
The patent combines multiple simulation approaches (thermodynamics, solid state formation kinetics, and CFD) into a unified hybrid model. This integration enables the model to simultaneously handle phase equilibrium calculations, particle formation kinetics, and fluid dynamics, thereby enabling model-based conception and design of precipitation assets while maintaining reliability for scaling from lab to industrial processes.
Solution Approach 2:
The hybrid model serves multiple functions: it performs thermodynamic equilibrium analysis, predicts solid formation kinetics, simulates fluid flow and mixing, and enables scale-up from laboratory to industrial conditions. This multi-functionality allows a single model framework to support the entire precipitation process design and optimization workflow.
2Ease of operation
If simple linearized models are used, then control strategy implementation is simplified, but dynamics and local resolution needed for chemical reactions are lost
Solution Approach 1:
The model incorporates dynamic equations that capture transient behavior of precipitation processes, including time-dependent particle formation, growth, and aggregation. The CFD component resolves spatially and temporally varying flow fields, concentration distributions, and mixing dynamics, providing accurate local resolution while maintaining computational tractability for control applications.
Solution Approach 2:
The patent transitions from simplified zero-dimensional or one-dimensional models to full three-dimensional CFD-based simulations that resolve spatial variations in flow, concentration, and particle formation throughout the reactor volume. This dimensional enhancement provides the local resolution necessary for accurate chemical reaction modeling while maintaining ease of operation through automated solution procedures.
3Adaptability or versatility
If trial-and-error methods are used for scaling, then flexibility is maintained, but resource usage and time are increased
Solution Approach 1:
The hybrid model enables preliminary simulation and optimization of precipitation processes at laboratory scale before actual scaling to industrial conditions. By performing virtual experiments and parameter optimization in silico, the model reduces the number of physical trials needed during scale-up, thereby improving productivity while maintaining the flexibility to adapt to different process conditions and equipment configurations.
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
This approach allows for improved process control, reduced resource usage, and optimized material properties by predicting and controlling particle formation, leading to more efficient scaling and reduced trial-and-error in chemical plant operations.
Implementation Method 1
a computational fluid dynamics (CFD) based numerical simulation for predicting a precipitation process
Implementation Method 2
wherein the physico-chemical white box model comprises at least one thermodynamics model
Implementation Method 3
at least one solid formation model
Implementation Method 4
for predicting a precipitation process
Data Source
AI summary
A computer-implemented method for controlling and/or monitoring equipment (110) of a chemical plant is proposed. The method comprises the following steps: a) specifying at least one parameter of a production process to be optimized; b) receiving input data via at least one input channel (126), wherein the input data comprises operating conditions of the production process, physical properties of a plant equipment layout, and at least one predicted parameter determined from a physico-chemical white box model (128), wherein the physico-chemical white box model (128) comprises at least one thermodynamics model (130), at least one solid formation model (132) and a computational fluid dynamics (CFD) based numerical simulation (134) for predicting a precipitation process; c) optimizing the specified parameter via at least one processing device (138), wherein the specified parameter is optimized by adapting the operating conditions of the production process and/or the physical properties of the plant equipment layout based on the predicted parameter.