Industrial Gas Plant Control for Intermittent Renewable Power
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Solution Overview
Problem
The variability and intermittency of renewable energy sources like wind, solar, and tidal power pose challenges for industrial gas plants, which require a constant power supply to maximize utilization and efficiency, particularly in ammonia production processes sensitive to energy input variations.
Innovation Solution
A method and system utilizing machine learning models to predict available renewable power resources by analyzing historical environmental and operational data, allowing for the optimal control of industrial gas plants and storage resources, such as hydrogen and energy storage systems, to maximize power utilization and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If renewable power sources (wind, solar, tidal) are used to power industrial gas plants, then environmental sustainability is improved, but power supply stability deteriorates due to natural variability and intermittency
Solution Approach 1:
The system performs preliminary actions by predicting future renewable power availability using machine learning models trained on historical environmental and operational data. These predictions enable advance planning of gas plant operations and storage resource utilization, allowing the system to prepare for variable power supply conditions before they occur, thus maintaining operational stability while using renewable energy sources
2Productivity
If variable renewable energy input is used in ammonia production, then green ammonia production is achieved, but production efficiency deteriorates due to sensitivity to energy input variations
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual renewable power generation against predicted values and using this information to adjust gas plant operations in real-time. The machine learning models are trained on historical operational data and continuously refined, enabling the system to adapt to variations in renewable energy input while maintaining optimal ammonia production efficiency
Solution Approach 2:
The system applies dynamics by enabling flexible, real-time adjustment of gas plant operational parameters in response to varying renewable power availability. The control system dynamically optimizes production schedules and storage utilization based on predicted power patterns, allowing the ammonia production process to adapt efficiently to the intermittent nature of renewable energy inputs
3Productivity
If machine learning models are used to predict power resources and control gas plants, then power utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by implementing an integrated control platform that performs multiple functions: predicting renewable power availability, optimizing gas plant operations, managing storage resources, and monitoring performance. This multi-functional approach consolidates what could be separate complex systems into a unified solution, improving power utilization efficiency while managing overall system complexity through integration
Data Source
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AI summary
There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.