Joint-Level Inflow Modeling for Solvable Process Optimization
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Solution Overview
Problem
Current methods for optimizing production in the sand oil industry, particularly in converting sand oil to Synthesis Crude Oil, face challenges with bilinear optimization problems that are difficult to solve due to their non-deterministic polynomial-time hard nature, leading to inefficiencies in throughput and quality of flow management.
Innovation Solution
A system and method that utilize mode-specific regression models to convert continuous flow rate and control variables into finite-valued variables, allowing the transformation of bilinear optimization problems into linear ones, enabling the identification of optimal flow rates and quality modulation through joint-level combinations and sub-modes within the manufacturing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bilinear optimization models are used to optimize both throughput and quality of flow, then optimization accuracy is improved, but solution time and computational complexity increase significantly
Solution Approach 1:
The patent segments the continuous control variables into discrete joint-level combinations, transforming the bilinear optimization problem into a mixed-integer linear program. This segmentation allows the use of efficient linear programming solvers while maintaining optimization accuracy across multiple stages of the manufacturing process.
Solution Approach 2:
The patent changes the parameter representation from continuous variables to discrete joint-level parameters. By defining specific joint-level combinations of control variables across different stages, the model transforms the mathematical structure from bilinear to linear, enabling faster solution times while preserving optimization accuracy.
2Adaptability or versatility
If continuous control variables are used for flow rate and quality optimization, then operational flexibility is improved, but problem solvability deteriorates due to bilinear complexity
Solution Approach 1:
The patent segments the continuous control space into discrete joint-level combinations across multiple stages. This segmentation maintains operational flexibility by preserving multiple feasible operating points while transforming the mathematical problem into a solvable mixed-integer linear program that can be efficiently optimized.
Solution Approach 2:
The patent introduces joint-level combinations as intermediary discrete variables that mediate between continuous control inputs and process outputs. This intermediary representation enables the use of linear programming techniques while still capturing the essential relationships in the manufacturing process.
3Manufacturing precision
If multiple stages with multiple components are modeled explicitly, then process accuracy is improved, but model complexity and solution difficulty increase
Solution Approach 1:
The patent segments the multi-stage manufacturing process into distinct stages with explicit joint-level combinations for each stage. This segmentation allows accurate representation of each stage's control variables while transforming the overall model into a structured mixed-integer linear program that can be solved efficiently despite the complexity of multiple stages and components.
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 enables efficient optimization of manufacturing processes by converting bilinear problems into solvable mixed-integer linear programs, improving the management of both throughput and quality of flow, thereby enhancing operational efficiency and reducing solution times.
Implementation Method 1
Steam can be used to generate HPW. One or more flow rates of steam usage, HPW flow rate, and HPW temperatures can interact with process flow in the plant operation rates via heat exchange.
Implementation Method 2
Preheating of the production fluids prior to entry into the first stage separator can be accomplished by direct mixing with HPW taken from the effluent of the first stage separator and of the second stage separator.
Data Source
AI summary
One or more systems, computer-implemented methods and/or computer program products to facilitate a process to monitor and/or facilitate a modification to a manufacturing process. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that identifies values of inflow data of one or more inflows of a set of inflows to a manufacturing process as control variables, and a computation optimization component that optimizes one or more intermediate flows, outflows or flow qualities of the manufacturing process using, for mode-specific regression models, decision variables that are based on a set of joint-levels of the control variables. An operation mode determination component can determine operation modes of the manufacturing process that are together defined by a set of joint-levels of the control variables.