Gas Pipeline Network Control System for Energy and Pressure Constraints
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Solution Overview
Problem
Current methods for controlling gas pipeline networks face challenges in efficiently calculating network flow solutions that satisfy energy consumption constraints and pressure constraints, often resulting in stranded molecules and venting of gas due to the complexity of nonlinear pressure drop relationships and nonconvex optimization programs.
Innovation Solution
A system and method that calculate minimum and maximum production rates at industrial gas production plants to set bounds on production rates, linearize the pressure drop relationship within these bounds, and use these linear models to compute network flow solutions that satisfy energy consumption, pressure, and demand constraints, with error bounding to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If nonlinear pressure drop relationships and nonconvex optimization programs are used to control gas pipeline networks, then accuracy of satisfying pressure and energy constraints is improved, but computational complexity and difficulty of calculating network flow solutions increases
Solution Approach 1:
The patent transforms the nonlinear pressure drop relationships into linear approximations by changing the mathematical parameters and form of the equations. This linearization allows the use of efficient linear programming techniques while maintaining sufficient accuracy for practical control applications, thereby reducing computational complexity without completely sacrificing constraint satisfaction accuracy.
Solution Approach 2:
The patent employs simplified linear models that can be quickly computed and discarded for each control interval, replacing the need for computationally expensive nonlinear optimization. These linear approximations are recalculated frequently at lower computational cost, enabling real-time control while avoiding the burden of solving complex nonlinear programs.
2Productivity
If linearized pressure drop models are used to calculate network flow solutions, then computational efficiency is improved, but accuracy of satisfying pressure constraints may deteriorate
Solution Approach 1:
The patent implements an iterative feedback mechanism where the linearized models are repeatedly solved and the results are used to update the linearization parameters. This feedback loop allows the system to converge toward more accurate solutions while maintaining the computational efficiency of linear methods, effectively bridging the gap between speed and accuracy.
Solution Approach 2:
The patent makes the linearization parameters dynamic rather than static, adjusting them based on operating conditions and previous solution results. This dynamic adaptation allows the linearized model to better track the actual nonlinear behavior across different operating points, improving accuracy while preserving computational efficiency.
Data Source
AI summary
Controlling flow of gas in an gas pipeline network, wherein flow of gas within each of the pipeline segments is associated with a direction (positive or negative). Processors calculate minimum and maximum production rates (bounds) at the gas production plant to satisfy an energy consumption constraint over a period of time. The production rate bounds are used to calculate minimum and maximum signed flow rates (bounds) for each pipeline segment. A nonlinear pressure drop relationship is linearized to create a linear pressure drop model for each pipeline segment. A network flow solution is calculated, using the linear pressure drop model, comprising flow rates for each pipeline segment to satisfy demand constraints and pressures for each of a plurality of network nodes over the period of time to satisfy pressure constraints. The network flow solution is associated with control element setpoints used to control one or more control elements.