Self-Learning Control Loop for Sensorless Energy Optimization
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Solution Overview
Problem
Existing control systems for temperature and flow control, such as those used in pumps and fans, often fail to optimize energy use and efficiency due to their inability to adapt to changing environments and demands, relying on external sensors and manual configuration, which can lead to suboptimal operation and increased energy consumption.
Innovation Solution
A self-learning control system that detects input and system variables, updates a model to predict optimal operation points, and adjusts the operation of operable elements to achieve setpoints, eliminating the need for external sensors and manual configuration by using internal detection and self-learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external sensors and manual configuration are used in control systems, then measurement precision and reliability are improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The control system automatically detects input variables and updates its own model without requiring external sensors or manual configuration. The system serves itself by using internal detection capabilities and self-learning algorithms to optimize operation, eliminating the need for additional measurement devices and manual setup.
Solution Approach 2:
The patent replaces physical external sensors with virtual sensing through mathematical models and algorithms. Instead of using mechanical or electronic sensor devices to detect variables, the system uses computational models to estimate and detect input variables based on available data, substituting physical measurement infrastructure with information processing.
2Ease of operation
If existing control systems are used, then ease of operation is improved, but adaptability to changing environments deteriorates
Solution Approach 1:
The control system dynamically adapts to changing environments by continuously updating its model based on detected input variables and system variables. The model evolves over time to reflect current operating conditions, allowing the system to maintain optimal performance across varying environmental conditions without manual reconfiguration.
Solution Approach 2:
The system uses feedback from detected input variables and system variables to continuously improve its model and operation. By monitoring actual system behavior and comparing it with model predictions, the system adjusts its parameters and operations to maintain optimality as environments change, creating a closed-loop adaptive control mechanism.
3Ease of operation
If manual configuration is used, then ease of operation is improved, but productivity and energy efficiency deteriorate
Solution Approach 1:
The system automatically optimizes its operation to maximize energy efficiency and productivity without requiring manual configuration or tuning. The self-learning control algorithm continuously identifies optimal operating parameters based on detected variables, eliminating the need for expert intervention while achieving superior energy efficiency compared to manually configured systems.
Solution Approach 2:
The patent introduces a self-learning control algorithm as an intermediary between the physical system and the control objectives. This software intermediary automatically translates detected system variables into optimized control actions, serving as a bridge that enables energy-efficient operation without requiring manual expertise or complex configuration procedures.
Data Source
AI summary
A control system for an operable system such as a flow control system or temperature control system. The system operates in a control loop to regularly update a model with respect at least one optimizable input variable based on the detected variables. The model provides prediction of use of the input variables in all possible operation points or paths of the system variables which achieve an output setpoint. In some example embodiments, the control loop is performed during initial setup and subsequent operation of the one or more operable elements in the operable system. The control system is self-learning in that at least some of the initial and subsequent parameters of the system are determined automatically during runtime.