Adaptive MINLP Process Control for Feasible Switching Paths
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for continuous process optimization, such as those in refineries and petrochemical plants, face inefficiencies due to the complexity and time-consuming nature of Mixed Integer Nonlinear Programming (MINLP) solvers, which are not optimized for processor and memory usage, leading to suboptimal decision-making in switching operations.
Innovation Solution
The implementation of online first-principles simulation techniques in conjunction with a MINLP solver, utilizing an oracle system with adaptive algorithms to manage infeasible solutions, reduce search spaces, and automatically generate feasible paths, thereby enhancing the robustness and efficiency of process optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional MINLP solvers search all possible regions for optimal solutions, then solution completeness is improved, but computational time and complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by performing feasibility analysis and generating initial groupings of process units before the main optimization search. The oracle component pre-evaluates which units can be feasibly switched on or off based on demand requirements, eliminating infeasible regions from the search space before the MINLP solver begins its optimization, thus reducing computational time while maintaining solution completeness.
Solution Approach 2:
The patent extracts and removes infeasible solution regions from the search space using the oracle component. By identifying and eliminating combinations of unit switchings that cannot satisfy demand requirements, the system reduces the number of regions the MINLP solver must evaluate, thereby decreasing computational time without sacrificing the reliability of finding the true optimal solution.
2Reliability
If conventional MINLP solvers evaluate all switching combinations, then optimal switching decisions are improved, but processor and memory efficiency deteriorate
Solution Approach 1:
The patent segments the optimization problem into two parts: a feasibility assessment phase handled by the oracle component and an optimization phase handled by the MINLP solver. The oracle pre-segments and eliminates infeasible unit combinations, allowing the MINLP solver to focus only on feasible regions, thus improving processor efficiency while maintaining the ability to find optimal switching decisions.
Solution Approach 2:
The oracle component serves as an intermediary between the process units and the MINLP solver. It pre-evaluates feasibility constraints and provides guidance to the solver, acting as a mediator that filters out infeasible configurations before they reach the optimization algorithm, thereby improving computational efficiency without compromising optimal decision-making.
3Reliability
If the search space includes all possible unit switching states, then feasibility detection is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary feasibility detection using the oracle component before the main optimization search. The oracle pre-evaluates which process units can be feasibly switched on or off based on demand requirements, creating an initial feasible search space that reduces computational complexity while maintaining reliable feasibility detection throughout the optimization process.
Solution Approach 2:
The patent extracts infeasible switching combinations from the search space using the oracle's feasibility analysis. By removing combinations that cannot satisfy demand requirements, the system reduces computational complexity while maintaining accurate feasibility detection, as the oracle continues to validate solutions against constraints during optimization.
Data Source
AI summary
Real-time dynamic process modeling in an online model-based process control computing environment. A solver system utilizes an oracle to implement adaptive algorithms for a mixed integer nonlinear programming (MINLP) solver and a nonlinear programming (NLP) solver.