Backfeeding Installation Compressor Control via Pressure Prediction
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Solution Overview
Problem
Current backfeeding installations in gas transport networks are inefficient due to variable pressure and flow rates in distribution networks, leading to partial operation and the need for on-site supervision, which limits the management and optimization of biomethane injection into transport networks.
Innovation Solution
A backfeeding installation with an automaton that controls compressor operation based on predicted pressure and gas quality changes, using dynamic learning processes, artificial intelligence, and remote data analysis to determine threshold values and loading rates, enabling remote operation and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple pressure regulation is used in the distribution network, then the backfeeding installation can be easily activated, but the operation becomes partially inefficient due to variable pressure and flow rates
Solution Approach 1:
The system performs preliminary actions by predicting future pressure changes in the distribution network before they actually occur. The prediction module uses historical data and network configuration to anticipate pressure variations, allowing the control system to prepare appropriate responses in advance, thereby maintaining operational efficiency despite variable network conditions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual pressure and flow rate data from the distribution network, comparing it with predicted values, and adjusting the backfeeding installation operation accordingly. This closed-loop control ensures the system adapts to real-time network conditions while maintaining optimal efficiency.
2Productivity
If the backfeeding installation operates continuously to maximize biomethane export, then productivity increases, but the variable network conditions cause partial inefficiency and require on-site supervision
Solution Approach 1:
The system performs self-service by autonomously monitoring its own operation parameters, predicting network conditions, and adjusting its own performance without requiring external intervention. The control system independently evaluates pressure predictions, flow rates, and backfeeding performance, making self-corrections to maintain optimal efficiency and reduce the need for on-site supervision.
Solution Approach 2:
The system replaces manual mechanical supervision with automated electronic control and prediction algorithms. Instead of requiring operators to physically monitor and adjust the backfeeding installation, the system uses sensors, data processing, and automated control mechanisms to manage operation, thereby increasing productivity while reducing the need for on-site presence.
3Device complexity
If pressure thresholds are set statically to simplify control, then device complexity is reduced, but the system cannot adapt to evolving network configurations and consumption patterns
Solution Approach 1:
The system transitions from static to dynamic control by continuously adjusting pressure thresholds and operational parameters based on real-time network conditions. The prediction module dynamically updates its models based on changing consumption patterns and network configuration, allowing the control system to adapt to evolving conditions while maintaining appropriate complexity levels through automated adjustment rather than manual reconfiguration.
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
The solution enhances the efficiency and optimization of biomethane injection by anticipating and adapting to network changes, reducing the need for on-site presence and improving the management of backfeeding operations, thereby increasing the capacity for biomethane production and storage.
Implementation Method 1
at least one compressor for compressing gas from a network
Data Source
AI summary
The invention relates to a backfeeding installation (30) which comprises:at least one compressor (21) for compressing gas from a network (15),an automaton (25) for controlling the operation of at least one compressor,a remote communication means (9) for receiving at least one instantaneous pressure value captured remotely on the network upstream of the backfeeding installation,a means (8) for predicting the evolution of the pressure in the network upstream of the backfeeding installation, depending, at least, on the pressure values received,a means (7) for determining a pressure threshold value for stopping or starting at least one compressor according to the prediction of the evolution of pressure,the automaton controlling the stopping or the operation of at least one compressor when the pressure at the inlet of each compressor is lower, or higher, respectively, than the pressure threshold value that was determined.