A novel radio controlled flow and pressure valve for pasteurization process operated through Deep reinforcement learning based Model predictive controller

Abstract

The present invention relates to a novel radio-controlled flow and pressure valve system specifically designed for the pasteurization process. This valve is equipped with a Deep Reinforcement Learning (DRL)-based Model Predictive Controller (MPC) for precise and adaptive control over fluid dynamics during pasteurization. The system utilizes wireless radio signals to remotely regulate the flow rate and pressure of the pasteurization medium, ensuring optimal temperature and quality standards. By integrating DRL, the valve continuously learns from environmental conditions and past control actions, adjusting in real time to improve efficiency and consistency of the process. The MPC framework enables future state predictions, optimizing valve operation to minimize energy consumption, enhance shelf life, and ensure product safety without human intervention. This intelligent control system improves the performance of traditional pasteurization processes, making it suitable for industrial-scale dairy and food production.

Specification

Description:This invention relates to a novel radio-controlled flow and pressure valve system specifically designed for the pasteurization process. This valve is equipped with a Deep Reinforcement Learning (DRL)-based Model Predictive Controller (MPC) for precise and adaptive control over fluid dynamics during pasteurization. The system utilizes wireless radio signals to remotely regulate the flow rate and pressure of the pasteurization medium, ensuring optimal temperature and quality standards. By integrating DRL, the valve continuously learns from environmental conditions and past control actions, adjusting in real time to improve efficiency and consistency of the process. The MPC framework enables future state predictions, optimizing valve operation to minimize energy consumption, enhance shelf life, and ensure product safety without human intervention. This intelligent control system improves the performance of traditional pasteurization processes, making it suitable for industrial-scale dairy and food production.

This invention relates to a novel radio-controlled flow and pressure valve system designed specifically for optimizing the pasteurization process. The system is operated through an advanced Deep Reinforcement Learning (DRL)-based Model Predictive Controller (MPC), which ensures precise and adaptive control over both fluid flow rate and pressure during the pasteurization process.
The radio-controlled valve operates wirelessly, enabling remote regulation of the flow and pressure in the pasteurization pipeline. It is equipped with a servo proportional mechanism that adjusts in real time based on temperature and pressure feedback from the pasteurization environment. The valve uses an electrical torque motor coupled with an encoder to control the spool's movement, providing fine-tuned control over fluid dynamics. This setup ensures that the system maintains optimal pasteurization conditions, even in the presence of disturbances and time delays, which are typical in real-world operations.
To further enhance the valve's functionality, the Deep Reinforcement Learning (DRL)-based Model Predictive Controller (MPC) is integrated into the system. The MPC anticipates future changes in the pasteurization process, adjusting valve operations accordingly to optimize energy efficiency, maintain product quality, and reduce the likelihood of deviations from target temperature and pressure set points. By leveraging real-time data, the controller adapts to fluctuations and disturbances, ensuring steady-state operation with minimal energy use.
In comparison to traditional on-off controllers, which often result in fluctuating temperature and pressure profiles, the proposed system provides a more stable and energy-efficient solution. The DRL-MPC intelligently adjusts system parameters, predicting process outcomes and making corrections to maintain optimal conditions. This predictive control helps avoid the inefficient energy usage and quality fluctuations seen with conventional control methods.
The control system is further enhanced using an Arduino-based Deep Learning Model Control (DLMC) MPC controller, which incorporates reinforcement learning (RL) agents. These RL agents, trained using the Double Deep Q-Network (DDQN) algorithm, make decisions in real time to maximize overall system performance. The RL agents evaluate various state-action pairs, learning from the environment and optimizing actions to minimize control errors. The system continuously adapts to changing conditions within the pasteurization process, ensuring precise flow and pressure control. The proposed DRL-MPC controller significantly reduces the time required to achieve steady-state operation, compared to traditional PID controllers, by minimizing the integral square error (ISE) and optimizing the flow and pressure control outputs in open loop as well as closed loop system.
, C , C , C , Claims:Claim 1:
A radio-controlled flow and pressure valve system for a pasteurization process, comprising a servo proportional valve that adjusts fluid flow and pressure based on real-time feedback, wherein the valve is controlled remotely through a wireless radio signal.
Claim 2:
The system of Claim 1, wherein the control mechanism is operated by a Deep Reinforcement Learning (DRL)-based Model Predictive Controller (MPC), which dynamically adjusts the valve's operation to optimize temperature, flow rate, and pressure during the pasteurization process.
Claim 3:
The system of Claim 2, wherein the DRL-based MPC utilizes a reinforcement learning algorithm to predict and adjust the valve operation, accounting for time delays, disturbances, and fluctuations in real-time process conditions.
Claim 4:
The system of Claim 1, further comprising an encoder coupled with the valve's torque motor to provide fine-tuned control over the movement of the main spool, ensuring precise adjustment of flow and pressure in the pasteurization pipeline.
Claim 5:
The system of Claim 2, wherein the DRL-based MPC controller incorporates Double Deep Q-Network (DDQN) algorithms to train reinforcement learning agents that optimize control actions, minimize errors, and maximize the reward in the pasteurization process.
Claim 6:
The system of Claim 1, further comprising an open-loop and closed-loop feedback control system to capture dynamic responses, tune controller parameters, and refine flow and pressure control based on real-time feedback from sensors placed in the pasteurization process.
Claim 7:
The system of Claim 2, wherein the DRL-based MPC optimizes the valve's operation by adjusting the state-action pair values in the reinforcement learning model, continuously learning from real-time environmental feedback.
Claim 8:
The system of Claim 1, wherein the wireless radio signal is used to remotely control multiple valves across different pasteurization zones, allowing for coordinated and automated flow and pressure management throughout the pasteurization process.

Documents