Process Optimization Server for Incremental Set-Point Alignment
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Solution Overview
Problem
Current methods for incremental process optimization in industrial plants between rigorous optimization runs often result in suboptimal set-point changes due to the use of conservative linear models, which fail to accurately represent nonlinear plant behavior, leading to increased workload on tracking control systems and potential product wastage.
Innovation Solution
A server system that utilizes a dimensionally invariant linear model derived from rigorous optimization models, with updated cost coefficients and incremental bounds, to perform quasi-dynamic optimization and generate incremental set-point changes, ensuring alignment with the rigorous optimization direction and reducing burdens on tracking control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If simplified linear models are used for incremental optimization between rigorous optimization runs, then computational speed is improved, but manufacturing precision deteriorates due to suboptimal set-point changes
Solution Approach 1:
The patent transforms the optimization problem by changing the parameter representation from absolute set-points to incremental adjustments. The linear model uses incremental changes in manipulated variables (ΔMV) and controlled variables (ΔCV) as parameters, allowing the system to achieve both computational efficiency and accuracy by focusing on small perturbations around the current operating point rather than solving the full nonlinear problem.
Solution Approach 2:
The optimization process is segmented into two distinct stages: (1) rigorous optimization runs that occur at steady-state to establish target set-points, and (2) incremental optimization runs that occur between steady-states to provide intermediate adjustments. This segmentation allows each method to be used where it is most effective, combining the accuracy of rigorous optimization with the speed of linear optimization.
2Device complexity
If conventional linear programming is used for incremental optimization, then device complexity is reduced, but productivity deteriorates due to increased workload on tracking control systems
Solution Approach 1:
The system implements a feedback mechanism where the linear optimization model continuously receives updated cost coefficients and constraints from the process state, and its outputs (incremental set-point changes) are fed back to the tracking control system. This feedback loop ensures that even though the linear model is simpler, it remains aligned with the actual process conditions and rigorous optimization direction, maintaining productivity.
Solution Approach 2:
The linear optimization model is made dynamic by updating its parameters (cost coefficients, constraints, and operating point) between rigorous optimization runs. Rather than using a static model, the system adapts the linear model to current process conditions, allowing it to provide accurate incremental guidance without requiring complex rigid structures.
3Reliability
If conservative linear models are used to avoid bounded regions, then reliability is improved, but manufacturing precision deteriorates due to suboptimal solutions
Solution Approach 1:
The system performs preliminary rigorous optimization at steady-state to establish accurate target set-points and cost coefficients before entering the incremental optimization phase. This preliminary action ensures that when the linear model operates in bounded regions between steady-states, it starts from a reliable foundation and remains directed toward the rigorous optimization target, combining reliability with precision.
Solution Approach 2:
The linear optimization model serves as an intermediary between the rigorous optimization engine and the tracking control system. It translates the rigorous optimization objectives into practical incremental adjustments that are valid within bounded regions, acting as a mediator that maintains both model reliability and solution accuracy by operating within its valid range while still achieving optimal results.
Data Source
AI summary
Some embodiments include a server system including a first logic module executable by a processor for receiving a data communication from an industrial control system coupled to a communications network. In some embodiments, the data communication comprises data or data streams associated with the industrial process. The program logic of the first logic module includes a model configured to receive a variable and iterate and converge an optimization problem to an optimization solution to a first level of optimization based at least in part on the variable and the data or data streams. A second logic module executable by the processor is operatively data-linked to the first logic module and utilizes a model to iteratively process data values of the optimization solution to a second level of optimization with an increased level of optimization over the first level of optimization.