Dual-Horizon Refinery Optimization for Demand Disturbance Rejection
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Solution Overview
Problem
Oil refineries face challenges in optimizing performance due to demand fluctuations and other disturbances, which conventional optimization approaches fail to adequately address, particularly in rejecting demand fluctuations and coordinating disturbance rejection across supply and feed sides.
Innovation Solution
A dual-horizon optimization process that balances supply-and-demand dynamics through a long-horizon optimized plan, guiding a short-horizon optimization process to minimize disturbances' impact on control targets, such as profitability, by adjusting operational parameters like blender outlet target flowrates and ideal resting values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional optimization approaches are used, then the optimization process is simpler, but the ability to reject demand fluctuations and coordinate disturbance rejection is insufficient
Solution Approach 1:
The optimization process is segmented into two distinct horizons: long-horizon optimization for strategic planning and supply-demand balancing, and short-horizon optimization for tactical disturbance rejection. This segmentation allows each horizon to specialize in specific functions, improving the system's ability to handle demand fluctuations while maintaining manageable complexity through modular design.
Solution Approach 2:
The long-horizon optimization performs preliminary actions by establishing optimal supply-demand balances and operational targets in advance. This preliminary planning creates a robust foundation that enables the short-horizon optimization to effectively reject demand fluctuations without requiring complex real-time calculations, thus improving reliability while controlling complexity.
2Stability of the object's composition
If long-horizon optimization is implemented, then supply-and-demand balancing improves, but the response time to immediate disturbances decreases
Solution Approach 1:
The dual-horizon approach segments the optimization timeline into long-horizon strategic planning and short-horizon tactical response. The long-horizon optimization ensures stable supply-demand balancing over extended periods, while the short-horizon optimization provides rapid response to immediate disturbances, thus achieving both stability and speed through temporal segmentation.
Solution Approach 2:
The long-horizon optimization performs preliminary supply-demand balancing to establish stable operational targets and constraints. This preliminary action creates a robust framework that enables the short-horizon optimization to quickly respond to disturbances within pre-defined boundaries, maintaining both stability and rapid response capability.
3Speed
If short-horizon optimization is used independently, then immediate disturbance response is faster, but long-term profitability optimization deteriorates
Solution Approach 1:
The optimization system is segmented into two hierarchical levels: short-horizon optimization handles immediate disturbance response with fast computation, while long-horizon optimization ensures long-term profitability through strategic planning. This segmentation allows each level to optimize for its specific time horizon without compromising the other, achieving both speed and reliability.
Solution Approach 2:
The long-horizon optimization performs preliminary strategic planning that establishes profitability-maximizing targets and operational constraints. This preliminary action guides the short-horizon optimization to respond to disturbances in a manner that maintains long-term profitability, ensuring that fast responses do not sacrifice overall economic performance.
4Adaptability or versatility
If multiple blenders are deployed to handle concurrent products, then product flexibility increases, but the complexity of coordination and control increases
Solution Approach 1:
The control system is segmented into blender-level individual control and plant-level coordinated optimization. Each blender is controlled independently based on its specific configuration and product requirements, while the long-horizon optimization coordinates overall plant operations. This segmentation enables high product flexibility through multiple blenders while managing coordination complexity through hierarchical control.
Solution Approach 2:
The long-horizon optimization performs preliminary coordination of multiple blenders by establishing optimal operational schedules and product allocation strategies in advance. This preliminary coordination reduces real-time complexity by pre-resolving conflicts and optimizing blender utilization, allowing individual blenders to operate with greater autonomy while maintaining overall plant flexibility.
Data Source
AI summary
Embodiments of the present disclosure provide for generating at least one optimized operational parameter associated with a processing plant, such as an oil refinery. Embodiments of the disclosure utilize a multi-horizon optimization process, for example that generates optimized operational parameter(s) based on at least a long-horizon optimized plan and a short-horizon optimized plan. Some embodiments utilize the multiple horizons to generate the optimized operational parameter(s) via different process(es) based on configuration(s) and/or characteristic(s) of a processing plant. The resulting optimized operational parameter(s) enable operation of the processing plant during the immediate term in a manner that is optimized specifically for the processing plant in its particular configuration.