Chemical Process Feed Coordination Under Downstream Constraints
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Solution Overview
Problem
Chemical plants face challenges in optimizing production processes due to complex interactions between upstream and downstream processes, leading to inefficiencies and conflicts between optimization goals and advanced process control (APC) models, which are often susceptible to instrumentation errors and do not fully implement optimum conditions.
Innovation Solution
A dynamic optimizer system that includes a maximum feed calculator and a feed coordinator, which communicate with standard section control applications to optimize both hot and cold sides of the chemical plant, determining maximum feed capacities and upstream production parameters to achieve optimal product mix and energy efficiency, using steady-state models and dynamic models to adjust operational targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard APC models are used for process control, then process stability is maintained, but optimization goals cannot be fully implemented due to instrumentation errors and model limitations
Solution Approach 1:
The patent introduces a dynamic optimization layer that acts as an intermediary between the APC models and the actual control system. This layer uses real-time plant data to dynamically adjust optimization targets and constraints, compensating for APC model limitations and instrumentation errors while maintaining process stability through coordinated control actions
Solution Approach 2:
The system implements continuous feedback loops where plant performance data is constantly monitored and fed back to the dynamic optimizer. This feedback mechanism allows the system to detect deviations caused by instrumentation errors and adjust optimization targets in real-time, ensuring both stability and optimal performance
2Measurement precision
If complex predictive models are used to characterize system behavior, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the complex predictive modeling task into multiple modular components, including separate modules for process dynamics modeling, constraint modeling, and objective function formulation. Each module can be independently developed, validated, and adjusted, reducing overall system complexity while maintaining high measurement precision through coordinated module interactions
3Productivity
If dynamic optimization adjusts operational targets in real-time, then productivity increases, but conflicts with APC models arise
Solution Approach 1:
The patent implements a dynamic optimization framework where operational targets and constraints are continuously adjusted based on real-time plant conditions. The system dynamically reconciles optimization goals with APC model predictions by adapting targets to current process states, enabling high productivity while maintaining model consistency through flexible, condition-dependent optimization strategies
Data Source
AI summary
System and method for chemical manufacture utilizing a dynamic optimizer for a chemical process including upstream and downstream processes. The dynamic optimizer includes a maximum feed calculator, operable to receive one or more local constraints on the downstream processes and one or more model offsets, and execute steady state models for the downstream processes in accordance with the local constraints and the offsets to determine maximum feed capacities of the downstream processes; and a feed coordinator, operable to receive the maximum feed capacities, and execute steady state models for the upstream processes in accordance with the maximum feed capacities and a specified objective function, subject to global constraints, to determine upstream production parameters for the upstream processes, which are usable to control the upstream processes to provide feeds to the downstream processes in accordance with the determined maximum feeds and the objective function subject to the global constraints.