IIoT CONNECTED EDGE COMPUTING HARDWARE BASED ON DIGITAL TWIN FOR PROCESS SYSTEMS

Abstract

ABSTRACT DIGITAL TWIN-IIOT-BASED INDUSTRIAL PROCESS CONTROL SYSTEM The present invention provides a system for real-time monitoring, predictive modeling, and bidirectional control of industrial processes, comprising of: a) edge computing hardware for localized data processing, model training, and control; b) multiple digital twin models as independent threads; c) a control process integrating sensor and model data, manipulating variables via final control elements; d) an IIoT server with REST API and OPC UA for bidirectional communication; e) a hybrid modeling module for dynamic model selection; and f) a predictive intelligence framework for anomaly detection and optimization. FIG. 2

Description

FIELD OF THE INVENTIONThe present invention relates to industrial automation and process control, specifically a system integrating Digital Twin technology with Industrial Internet of Things (IIoT) capabilities.This system enables real-time monitoring, predictive modeling, and bidirectional control of both linear and nonlinear industrial processes using localized edge computing, eliminating reliance on external cloud servers. TECHNICAL BACKGROUND OF THE INVENTIONIndustrial process control is essential for ensuring efficiency, safety, and reliability across sectors like chemical processing, manufacturing, and energy production. Traditional systems rely on static mathematical models and manual interventions, which are inadequate for managing complex, nonlinear processes with dynamic behavior. The integration of Digital Twin (DT) and Industrial Internet of Things (IIoT) technologies haVE advanced real-time monitoring and predictive analytics, but existing solutions face significant challenges.From prior art technology, it is known that digital twin framework for network optimization exists, focusing on monitoring and security attestation, but it lacks control applications for industrial processes or integration with Supervisory Control and Data Acquisition (SCADA) systems. Likewise in prior art an IIoT-based digital twin system for resource allocation is known, relying on centralized servers, which introduces latency and single-point failure risks. However, prior art technology is known to have some major noteworthy drawbacks as below: • Lacks industrial process control and SCADA integration. • Uses centralized servers, risking latency. • Focuses on monitoring, not control. • Lacks control and edge capabilities. The present invention overcomes these limitations by providing a specially designed Digital Twin-IIoT-based system operating on edge computing hardware. It introduces hybrid modeling for nonlinear processes, real-time bidirectional control, and predictive intelligence for anomaly detection and autonomous response, offering a robust, scalable, and secure solution for industrial automation.OBJECTIVES OF THE INVENTIONThe principal objective of the present invention aims to iterate on current industrial process control by improving efficiency, reducing manual intervention, and incorporating predictive intelligence. By integrating automation, real-time monitoring, and intelligent decision-making, the system according to the present invention, enhances operational performance while ensuring safety and reliability.Another objective of the present invention is to simplify and accelerate industrial process control. By automating complex control operations, the system according to the present invention, minimizes operator workload and ensures faster, more accurate execution of industrial processes. With real-time data processing and intelligent decision-making capabilities, operational efficiency is significantly improved, reducing the reliance on manual adjustments.Another objective of the present invention is to reduce on-site dependency by enabling remote monitoring and control. The operators can supervise processes from anywhere, eliminating the need for constant physical presence at industrial plants. This not only improves workplace safety by reducing exposure to hazardous environments but also enhances overall efficiency by minimizing fatigue-induced errors. By leveraging automation and remote accessibility, industries can optimize their workforce while maintaining seamless process execution.Another objective of the present invention is to facilitate real-time control and decision-making. The system according to the present invention implements a bidirectional data exchange mechanism. This push-pull communication model enables continuous process monitoring and adjustments from any location. Live data streaming allows operators to visualize ongoing processes and make necessary modifications remotely. The integration of Industrial Internet of Things (IioT) ensures secure and efficient data flow, empowering industries with enhanced operational flexibility and decision-making capabilities.Another objective of the present invention is to develop a self-updating Digital Twin, which continuously refines its model using real-time data. The Digital Twin adapts dynamically, enabling precise predictions of process behavior. By forecasting potential deviations, operators can proactively adjust parameters, optimizing performance and reducing inefficiencies. This predictive modelling capability ensures smoother operations and enhances overall system reliability.Yet another objective of the present invention is to implement predictive intelligence to prevent emergency failures. Instead of reacting to malfunctions after they occur, the system according to the present invention detects anomalies in advance, allowing for timely interventions. Predictive maintenance techniques identify potential breakdowns before they disrupt operations, reducing downtime and improving safety. This proactive approach minimizes operational risks and ensures a stable industrial environment.How the foregoing objectives are achieved will be clear from the following description. In this context it is clarified that the description provided is non-limiting and is only by way of explanation.SUMMARY OF THE INVENTIONAccordingly, the present invention provides a system for real-time monitoring, predictive modeling, and bidirectional control of industrial processes, comprising of:a) edge computing hardware for localized data processing, model training, and control; b) multiple digital twin models as independent threads; c) a control process integrating sensor and model data, manipulating variables via final control elements; d) an IIoT server with REST API and OPC UA for bidirectional communication; e) a hybrid modeling module for dynamic model selection; andf) a predictive intelligence framework for anomaly detection and optimization.The present invention also provides a method for controlling an industrial process, comprising the steps of:a) collecting real-time sensor data; b) processing data on edge hardware to update digital twins; c) selecting models via hybrid modeling; d) integrating data for control actions; e) enabling bidirectional communication via IIoT server; andf) analyzing data for anomaly detection and optimization.BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGSThe nature and scope of the present invention will be better understood from the accompanying drawings, which are by way of illustration of a preferred embodiment and not by way of any sort of limitation. In the accompanying drawings:-Figure 1 illustrates the software architecture deployed on edge computing hardware in accordance with the present invention.Figure 2 depicts the control system architecture, with an IIoT server as the gateway between off-site user sessions, the experimental setup, and digital twin models in accordance with the present invention.DETAILED DESCRIPTION OF THE INVENTION Having described the main features of the invention above, a more detailed and non-limiting description of a preferred embodiment will be given in the following paragraphs with reference to the accompanying drawings.First the salient aspects of the present invention is described and then invention is described in greater detail.The invention provides a Digital Twin and IIoT-based industrial process control system for real-time monitoring, predictive modeling, and bidirectional control without external cloud servers. Utilizing edge computing hardware as a base station, the system ensures low latency, enhanced security, and operational continuity in low-connectivity environments. A hybrid modeling approach dynamically selects multiple digital twin models based on real-time process conditions, improving accuracy for nonlinear processes. Additionally, all digital twin processes run simultaneously and are accessible by the user so the user can monitor state estimate of the process by all the available digital twin processes. Push-pull communication enables remote monitoring and control via a secure application.The digital twin and Industrial internet of things (IIoT) based system is configured for real-time monitoring, predictive modeling, and bidirectional control of industrial processes. The control system is designed for applications in industries such as chemical processing, manufacturing, power generation, pharmaceuticals, food and beverage, and water treatment, offering a scalable, secure, and cost-effective solution for autonomous process optimization and remote accessibility.A predictive intelligence framework continuously analyzes data to detect anomalies, predict failures, and optimize processes autonomously. During emergencies, the system triggers alerts across logged-in devices and executes corrective actions using predictive models, with manual override capabilities. Scalable and supporting multi-user access with role-based authentication, the system is adaptable to industries like chemical processing, power generation, pharmaceuticals, food and beverage, and water treatment, offering a cost-effective, secure, and efficient solution.The present invention provides a system for real-time monitoring, predictive modeling, and bidirectional control of industrial processes, comprising of:a) edge computing hardware for localized data processing, model training, and control; b) multiple digital twin models as independent threads; c) a control process integrating sensor and model data, manipulating variables via final control elements; d) an IIoT server with REST API and OPC UA for bidirectional communication; e) a hybrid modeling module for dynamic model selection; andf) a predictive intelligence framework for anomaly detection and optimization.The edge computing eliminates cloud dependency, reducing latency and risks. The digital twins include first-principle, transfer function, and data-driven models, updated via machine learning. The control process uses a multiplexer for setpoint selection while the d IIoT server supports multi-user access with role-based authentication. The predictive intelligence triggers alerts and autonomous actions.The method for controlling an industrial process, comprises the following steps: a) collecting real-time sensor data; b) processing data on edge hardware to update digital twins; c) selecting models via hybrid modeling; d) integrating data for control actions; e) enabling bidirectional communication via IIoT server; andf) analyzing data for anomaly detection and optimization.The hybrid modeling switches between model types for accuracy and edge processing ensures low-connectivity operation. The method includes alerts and autonomous corrections.Now the invention is explained in greater detail with reference to the accompanying drawings.The software architecture deployed on edge computing hardware is shown in figure 1. Multiple digital twin models run as independent process threads, ensuring system stability by isolating errors. A control process integrates real-time sensor data and digital twin outputs, applying algorithms like Proportional-Integral-Derivative (PID) or Model Predictive Control (MPC) to manipulate process variables via final control elements (e.g., valves, pumps). A server program establishes a REST API, enabling secure client access to process data and real-time parameter adjustments via HTTP requests. OPC UA protocols ensure interoperability with industrial automation systems.The control system architecture, with an IIoT server as the gateway between off-site user sessions, the experimental setup, and digital twin models is shown in figure 2. Here, the Off-site users access the system via secure API endpoints. The IIoT server handles data exchange, logging data to a database and sending control signals to the physical setup, such as a Single Input Single Output (SISO) spherical tank system. Microcontrollers interpret sensor data and convert control outputs into electrical signals for final control elements. Digital twins, synchronized with sensor data, run in parallel, with a multiplexer (MUX) selecting the appropriate model output as the setpoint for the controller, adjusting parameters like Kc, Ti, and Td for precise control.System ArchitectureThe system operates on edge computing hardware, typically an industrial computer with a multi-core processor, serving as the base station for data processing, model training, and control execution. This eliminates cloud dependency, addressing latency and cybersecurity issues noted in prior art (e.g., WO2024213402A1). The architecture comprises:Digital Twin ModelsMultiple digital twin models represent the physical process, tailored for linear and nonlinear dynamics:First-Principle Models: Derived from physical laws, such as mass balance (( (bacK slash)frac{dm}{dt} = (bacK slash)dot{m}{in} - (bacK slash)dot{m}{out} )) for a tank system or reaction kinetics (( (bacK slash)frac{dC}{dt} = k C_A C_B )) for a chemical reactor. These models are accurate for well-defined linear processes but may require calibration for nonlinear regions.Transfer Function Models: Developed from experimental data, e.g., a second-order model ( G(s) = (bacK slash)frac{K}{(s + a)(s + b)} ), capturing steady-state behavior for linear or mildly nonlinear processes.Data-Driven Models: Neural networks or regression models trained on historical data, excelling in capturing nonlinear dynamics and adapting to process changes. The equations mentioned here are generic equations that will have to adapted to the specific process when deployed to a plant.Each model runs as an independent thread (Figure 1), ensuring robustness. For example, in a nonlinear process like a distillation column, a data-driven model handles transient vapor-liquid equilibrium, while a first-principle model manages steady-state operation. Models are synchronized with sensor data at 100 Hz, enabling fallback control if sensors fail.Control ProcessThe control process integrates sensor data and digital twin outputs, using algorithms like PID: [ u(t) = K_c (bacK slash)left( e(t) + (bacK slash)frac{1}{T_i} (bacK slash)int e(t) dt + T_d (bacK slash)frac{de(t)}{dt} (bacK slash)right) ] or MPC, which optimizes control actions based on a cost function: [ J = (bacK slash)sum_{k=1}N (y_k - r_k)2 + (bacK slash)lambda (bacK slash)sum_{k=1}M (bacK slash)Delta u_k2 ] where ( y_k ) is the process output, ( r_k ) is the setpoint, and ( (bacK slash)lambda ) balances control effort. The MUX (Figure 2) selects the optimal model output based on process conditions, such as temperature or flow rate ranges, ensuring accurate control for both linear (e.g., steady-state flow) and nonlinear (e.g., turbulent flow) processes.IIoT Server and Bidirectional ControlThe IIoT server, depicted in Figure 2, facilitates bidirectional control via a REST API and OPC UA protocols. Operators access a secure web or mobile application to monitor live data (e.g., temperature, pressure) and adjust setpoints (e.g., valve position). The server supports 200 concurrent users with response times under 50 ms, using TLS encryption and OAuth 2.0 for security. Number of concurrent users and response times of the server to the user are also dependent on the physical separation from the plant, but the controller operation is independent of these factors. For example, in a chemical reactor, an operator remotely increases coolant flow from 5 to 8 L / min, with the command executed in 100 ms. OPC UA ensures integration with SCADA systems, enabling seamless data exchange.Data Logging and Multi-User AccessProcess data, including sensor readings, model predictions, and control actions, are logged to a database at 1-second intervals, supporting up to 1 GB of historical data. Multi-user access with role-based authentication (e.g., operator, supervisor, maintenance) allows collaborative control, with operators adjusting parameters and supervisors reviewing logs.Predictive ModelingThe predictive modeling framework is central to the system's ability to manage linear and nonlinear processes autonomously. It comprises machine learning algorithms, such as Long Short-Term Memory (LSTM) networks for time-series prediction and Isolation Forest for anomaly detection, running on the edge hardware. The framework operates in three stages:Data Acquisition and PreprocessingSensors collect data at 100-1000 Hz, depending on the process and the data is preprocessed using filters (e.g., Kalman filter) to remove noise, ensuring model accuracy. For a nonlinear process like a chemical reactor, data includes temperature (20-200°C), pressure (1-10 bar), and concentration (0-1 mol / L).Model Training and UpdatingDigital twins are trained and updated on the edge hardware:- Linear Processes: Transfer function models are trained using least-squares regression on 10,000 data points, achieving 98% accuracy for steady-state conditions (e.g., constant flow in a pipeline).- Nonlinear Processes: LSTM networks are trained on 50,000-100,000 data points, capturing dynamic behaviors like temperature spikes in a reactor. Models are updated every 30 minutes or upon detecting a 10% deviation in process variables, using incremental learning to minimize computational overhead (e.g., 500 MB memory).The hybrid modeling approach selects the optimal model based on a decision tree algorithm, evaluating process conditions (e.g., temperature > 150°C selects data-driven model). For example, in a distillation column, the system switches from a first-principle model at steady state (100 bar) to a data-driven model during pressure surges (120 bar).Anomaly Detection and Failure PredictionIn a chemical production facility, shell-and-tube heat exchangers are critical for temperature regulation in exothermic reactions. Over time, fouling causes non-linear degradation in heat transfer efficiency. The system uses LSTM-based digital twins trained on temperature, flow rate, and pressure drop data to predict fouling onset.When the predicted outlet temperature deviates from the expected digital twin output by more than 8% and the pressure drop increases by 15%, the system triggers a fouling risk score dependent on the extent of deviation and weightage of each process variable set by the process engineer (on a 0-1 scale). This allows maintenance teams to schedule cleaning 20 hours before performance drops below critical limits, reducing unplanned shutdowns. During transient phases (e.g., startup or chemical switchovers), the control strategy switches from a first-principle model to a data-driven twin, ensuring accuracy during dynamic behavior.In a thermal power plant, maintaining consistent feedwater flow to the boiler is essential. A linear transfer function model is used to represent pump behavior under normal conditions. The system continuously monitors vibration, motor current, and discharge pressure from the feedwater pump.Using a hybrid approach, the system compares real-time sensor readings with both the transfer function and data-driven model predictions. The system detects anomalies when the current draw increases by 10% without corresponding increases in pressure output. The system flags a risk of failure score dependent on the deviations of the measure process variables from the predicted linear transfer function model, indicating impending bearing failure due to misalignment or wear.As a result, maintenance is performed 12 hours prior to mechanical failure, preventing steam supply interruption and ensuring uninterrupted power generation. Control continuity is maintained using fallback control from the most stable digital twin.Bidirectional Remote ControlBidirectional control enables operators to monitor and adjust processes remotely without cloud servers. The IIoT server processes commands locally, ensuring low latency (50-100 ms). For linear processes, such as maintaining a constant flow rate, operators set a setpoint (e.g., 100 L / min), and the PID controller adjusts the pump speed. For nonlinear processes, like a chemical reactor, the system uses predictive model outputs to adjust multiple variables (e.g., coolant flow and reactant feed) simultaneously. The application supports:- Real-Time Monitoring: Displays live data (e.g., temperature, pressure) updated every 500 ms.- Control Adjustments: Operators modify setpoints via sliders or numeric inputs, with changes applied in 100 ms.- Emergency Overrides: Manual overrides bypass autonomous actions, executed in 50 ms.For example, in a water treatment plant, an operator remotely increases pH dosing from 2 to 3 L / min, with the system confirming execution within 80 ms. The edge-based approach ensures operation in low-connectivity areas, unlike cloud-dependent systems as known in prior art.Localized ProcessingEdge computing eliminates cloud dependency, processing 10,000 samples with 50 ms latency. For instance, a neural network model with 1 million parameters trains in 5 minutes on a 16-core processor, using 8 GB RAM. Data remains within the local network, secured by data encryption, reducing cybersecurity risks by 40% compared to cloud systems.Data Table: Predictive Modeling and Control PerformanceMetric Linear Process Nonlinear Process DescriptionPrediction Accuracy 98% 95% Percentage of predictions within ±5% of actual valuesAnomaly Detection Rate 99% 98% Percentage of anomalies detected before disruptionFailure Prediction Time 30 min 20 min Time before failure detectionControl Response Time 50 ms 80 ms Time to execute control actionLatency Reduction 35% 30% Reduction compared to cloud-based systemsModel Update Frequency 1 / hour 2 / hour Frequency of model retrainingData Processing Rate 10,000 samples 8,000 samples Maximum sensor data processing rateThe present invention is applicable to chemical processing, power generation, pharmaceuticals, food processing, and water treatment for precise, autonomous control.From the foregoing it would be clear that all objectives of the present invention have been met.The present invention has been described with reference to some drawings and a preferred embodiment purely for the sake of understanding and not by way of any limitation and the present invention includes all legitimate developments within the scope of what has been described herein before and claimed in the appended claims.

Claims

1. A system for real-time monitoring, predictive modeling, and bidirectional control of industrial processes, comprising of: a) edge computing hardware for localized data processing, model training, and control; b) multiple digital twin models as independent threads; c) a control process integrating sensor and model data, manipulating variables via final control elements; d) an IIoT server with REST API and OPC UA for bidirectional communication; e) a hybrid modeling module for dynamic model selection; and f) a predictive intelligence framework for anomaly detection and optimization.

2. The system as claimed in claim 1, wherein said edge computing eliminates cloud dependency, reducing latency and risks.

3. The system as claimed in claim 1, wherein said digital twins include first-principle, transfer function, and data-driven models, updated via machine learning.

4. The system as claimed in claim 1, wherein the said control process uses a multiplexer for setpoint selection.

5. The system as claimed in claim 1, wherein the said IIoT server supports multi-user access with role-based authentication.

6. The system as claimed in claim 1, wherein said predictive intelligence triggers alerts and autonomous actions.

7. A method for controlling an industrial process, comprising the steps of: a) collecting real-time sensor data; b) processing data on edge hardware to update digital twins; c) selecting models via hybrid modeling; d) integrating data for control actions; e) enabling bidirectional communication via IIoT server; and f) analyzing data for anomaly detection and optimization.

8. The method as claimed in claim 7, wherein hybrid modeling switches between model types for accuracy.

9. The method as claimed in claim 7, wherein edge processing ensures low-connectivity operation.

10. The method as claimed in claim 7, including alerts and autonomous corrections.