Description:FORM 2
THE PATENT ACT, 1970
(39 OF 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. Title of the invention: “Industrial Non-Linear Model Process control and Optimization with Artificial Intelligence and machine learning”
2. Applicants:
NAME NATIONALITY ADDRESS
CEREBULB (INDIA) PRIVATE LIMITED Indian 7, Nakshatra Bungalow, vrundavan party plot road, Kens Villa, Nikol, Ahmedabad, Gujarat, 380049

3. Preamble to the description
COMPLETE SPECIFICATION
The following specification particularly describes the invention and the manner in which it is to be performed:

 
Field of the Invention
The present invention relates to the field of industrial process control and automation systems. More specifically, the present invention pertains to advanced process control methodologies utilizing artificial intelligence and machine learning techniques for real-time optimization of industrial processes. The present invention further relates to self-learning control systems that eliminate the need for conventional physical PID (Proportional-Integral-Derivative) controllers by implementing predictive algorithms that autonomously determine optimal operating parameters for complex industrial environments such as manufacturing plants, chemical processing facilities, power generation systems, and similar process-intensive industries where precise control of multiple variables is critical for operational efficiency, product quality, and cost reduction.
Background of the Invention
Industrial process control systems have traditionally relied on Proportional-Integral-Derivative (PID) controllers as the primary means of maintaining desired operational parameters. These conventional control systems, while functional, present numerous limitations in modern industrial environments that demand higher efficiency, reduced resource consumption, and consistent product quality.
Traditional PID controllers require extensive manual tuning by experienced operators to achieve acceptable performance. This tuning process is time-consuming, subject to human error, and often results in suboptimal parameter settings that must be regularly adjusted as process conditions change. Once implemented, these controllers operate reactively, responding to deviations only after they occur rather than preventing them proactively.
The limitations of conventional control systems are further compounded in complex industrial processes that involve multiple interrelated variables and constraints. Traditional PID controllers struggle to handle these multi-variable systems effectively, as they typically operate as independent control loops with limited ability to account for the interactions between different process parameters. This lack of coordination often leads to competing control actions, oscillations, and overall process instability.
Another significant drawback of current systems is their heavy dependence on operator intervention. Plant operators must regularly monitor process conditions and manually adjust setpoints to maintain optimal performance as production requirements change. This reliance on human decision-making introduces variability in process control, increases the potential for errors, and requires continuous attention from skilled personnel who could otherwise focus on higher-value tasks.
Additionally, conventional control systems lack the ability to learn from historical performance data and adapt to changing process conditions automatically. This inability to self-optimize means that processes often operate at less-than-ideal efficiency, resulting in unnecessary energy consumption, raw material waste, quality variations, and increased production costs.
As industrial operations face increasing pressure to maximize efficiency, minimize environmental impact, and maintain consistent product quality while reducing operational costs, there is a critical need for more advanced process control methodologies. The limitations of traditional PID-based systems highlight the necessity for innovative approaches that can leverage modern computational capabilities and data analysis techniques to provide autonomous, predictive, and self-optimizing control solutions for complex industrial processes.
Objective of the Invention
The main objective of the present invention is to provide an advanced Model Predictive Control (MPC) system enhanced with Artificial Intelligence and Machine Learning capabilities that eliminates the need for physical PID controllers in industrial processes.
Another objective of the invention is to implement a self-learning control algorithm that dynamically adjusts to changing process conditions without manual intervention.
Yet another objective of the present invention is to create a predictive control system that proactively adjusts process parameters based on AI/ML predictions rather than reacting to deviations after they occur.
Yet another objective of the present invention is to develop a control system capable of handling multiple process variables and constraints simultaneously, accounting for their interactions to maintain overall process stability.
Yet another objective of the present invention is to establish an autonomous system that eliminates the need for operator intervention in setting process parameters and control setpoints.
Yet another objective of the present invention is to implement continuous self-optimization capabilities that leverage historical and real-time data to determine optimal operating conditions for maximum efficiency and product quality.
Yet another objective of the present invention is to reduce resource consumption, energy usage, and production costs through more precise and efficient process control methodologies.
Yet another objective of the present invention is to provide comprehensive performance monitoring capabilities that enable real-time evaluation of system efficiency and process stability.
Summary of the present invention
The present invention provides an AI-powered Model Predictive Control (MPC) system for industrial process optimization that eliminates the need for conventional physical PID controllers by implementing a self-learning control algorithm. The system autonomously determines optimal setpoints using Artificial Neural Network and Extreme Gradient Boosting algorithms that analyze historical and real-time data from multiple industrial sources. It continuously optimizes processes by predicting optimal operating parameters based on product specifications, calculating appropriate control variables, and dynamically adjusting outputs without operator intervention. The present invention handles multiple process variables simultaneously, accounts for their interactions, provides real-time performance monitoring, and features a comprehensive user interface for visualization of process parameters, predictions, and metrics. The present invention significantly improves operational efficiency, reduces resource consumption, enhances product quality consistency, and minimizes human errors in industrial control applications.

Brief description of drawings
For the better understanding of the present invention, the respective drawings are as below:
Figure 1 shows the AI/ML Model Building Approach with input layer, hidden layers, and output layer structure for the artificial neural network and extreme gradient boosting algorithms.
Figure 2 shows the Model Predictive Control System Architecture with interconnections between data collection, AI/ML prediction, MPC optimization, and performance monitoring modules.
Figure 3 shows the Auto-Tuning Process Flow Diagram illustrating the bump test method for determining PID parameters using Ziegler-Nichols tuning rules.
Figure 4 shows the User Interface for Input Data Source with data source selection options and current parameter values for air, water, steam, and product categories.
Figure 5 shows the User Interface for System Operation featuring integrated panels for AI/ML predictions, control parameters, process outputs, and performance metrics.
Detailed description of the invention
The present invention relates to an advanced Model Predictive Control (MPC) system with Artificial Intelligence and Machine Learning (AI/ML) capabilities designed to optimize industrial processes autonomously. This detailed description provides a comprehensive explanation of the system's architecture, components, methodologies, algorithms, and operational procedures that collectively enable superior process control compared to conventional systems.
System Architecture Overview
The present invention comprises a comprehensive system architecture with several interconnected modules, each performing specific functions while communicating seamlessly with other components. The primary modules include:
1. Data Collection Module
2. Artificial Intelligence and Machine Learning (AI/ML) Prediction Module
3. Advanced Model Predictive Control (MPC) Module
4. Performance Monitoring Module
5. User Interface Module
These modules operate in a coordinated manner to achieve autonomous process optimization without requiring manual intervention. The following sections provide detailed explanations of each module's structure, functionality, and technical implementation.
Main embodiment of the present invention is a method for Industrial Non-Linear Model process control and real-time optimization with Artificial Intelligence and machine learning comprising of:
a. Data collection module configured to gather real-time and historical data from multiple industrial sources and ensures seamless integration with diverse data streams, providing a unified platform for efficient monitoring, analysis, and decision-making across industrial environments;
b. Artificial Intelligence and Machine Learning (AI/ML) prediction module configured to analyze real-time and historical data to predict optimal setpoints for process parameters using at least one machine learning algorithm;
c. Advanced Model predictive control module configured to calculate control variables based on predicted setpoints and actual process values;
d. Performance monitoring module configured to statistical methods to calculate key performance indicators and evaluate system efficiency through real-time metrics; and
e. User Interface Module provides a comprehensive visual representation of system operations, facilitating interaction, monitoring, and analysis;
wherein the said model eliminates the need for physical Proportional-Integral-Derivative controllers by implementing a self-learning control algorithm which operates autonomously without manual intervention.
Another embodiment of the present invention is the said data collection module interfaces with PI Servers to access historical trends and real-time sensor data, OPC servers as the standard communication bridge between industrial hardware devices and software applications, and PLCs monitor inputs and execute programmed logic to control outputs; and said organizes collected data into logical categories based on process air parameters, water parameters, steam parameters, and product parameters.
Another embodiment of the present invention is the said AI/ML prediction module is a hybrid approach of Artificial Neural Network and Extreme Gradient Boosting algorithms using a weighted ensemble technique.
Another embodiment of the present invention is the said advanced model predictive control module implements an auto-tuning algorithm based on the Ziegler-Nichols method to determine optimal control parameters without manual intervention.
Another embodiment of the present invention is the said performance monitoring module calculates statistical process measures including actual value, set point, mean value, and standard deviation, and determines control limits.
Another embodiment of the present invention is the said performance monitoring module incorporates an intelligent alarm management system comprises Critical Alarm, Warning Alarms and Advisory Notifications.
Another embodiment of the present invention is said process initiated with model configuration, establishing connections to data sources, Historical data is retrieved to establish baseline operation patterns, Initial AI/ML models are loaded or trained, and Auto-tuning is performed to determine initial PID parameters.
Data Collection Module
The Data Collection Module serves as the backbone of the system, responsible for gathering both real-time and historical data from a wide range of industrial sources. This module ensures seamless integration with diverse data streams, providing a unified platform for efficient monitoring, analysis, and decision-making across industrial environments. One of the primary integration points is the PI Server, a widely adopted data historian platform used across industries to store large volumes of time-series data. By interfacing with PI Server, the module can access historical trends and real-time sensor data, enabling deep analysis and predictive insights that enhance operational efficiency.
In addition, the module connects with OPC Servers, which serve as the standard communication bridge between industrial hardware devices and software applications. Through this integration, the system can reliably exchange data with various equipment and control systems, supporting interoperability in complex industrial setups. The module also features direct integration capabilities with Programmable Logic Controllers (PLCs), which are essential in industrial automation. By collecting data from PLCs, which monitor inputs and execute programmed logic to control outputs, the module ensures accurate, real-time visibility into the operational status and performance of industrial processes.
Beyond standard sources, the Data Collection Module is designed to accommodate other custom data sources. This includes proprietary systems or specialized data collection solutions tailored to specific industrial needs. With customizable connectors and adaptable architecture, the module can be extended to capture data from unique environments, ensuring scalability and future-proofing the system for evolving industrial requirements. This flexible architecture allows the system to be deployed across diverse industrial environments without requiring significant modifications to existing infrastructure.
Parameter Categorization
Data collected from various sources is systematically organized into logical categories based on process characteristics:
Air Parameters
• Air Temperature: Environmental or process air temperature measurements in degrees Celsius or Fahrenheit.
• Air Pressure: Atmospheric or process-specific air pressure measurements in appropriate units (e.g., bar, psi).
• Air Flow: Volumetric or mass flow rate measurements for air streams in appropriate units (e.g., m³/s, kg/h).
Water Parameters
• Flow Rate: Quantitative measurement of water flow through the system (e.g., 120 TPH).
• Pressure: Measurement of water pressure at various points in the process (e.g., 3.5 kg/cm²).
• Temperature: Thermal measurement of water in the system (e.g., 100°C).
Steam Parameters
• Flow Rate: Quantitative measurement of steam flow (e.g., 380 TPH).
• Pressure: Measurement of steam pressure at different process locations (e.g., 5.7 kg/cm²).
• Temperature: Thermal measurement of steam in the system (e.g., 300°C).
Product Parameters
• Flow Rate: Production rate measurement (e.g., 8 TPH).
• Temperature: Product temperature monitoring (e.g., 61.89°C).
• Grade: Classification identifier for product type or specification (e.g., Grade A).
• Batch: Sequential identifier for production batches (e.g., Batch 045).
• Blend: Mixture specification for product components (e.g., XYZ, FTU, BQA).
Data Pre-processing
Once data is collected from various industrial sources, it passes through the Data Pre-processing stage to ensure quality and consistency before being utilized by downstream modules. This critical step refines the raw data, eliminating inaccuracies and aligning it with the system’s analytical requirements. The first step involves outlier detection and removal, where statistical methods are applied to identify and filter out anomalous data points. These outliers, if left unaddressed, can distort analytical outcomes and degrade the overall performance of the system. By cleaning such irregularities, the module ensures a more accurate and reliable data set.
Next, the module tackles missing value handling. Industrial data streams often suffer from gaps due to sensor faults, communication errors, or system downtimes. Sophisticated algorithms are employed to address these missing values, using interpolation techniques or appropriate substitutions that maintain data continuity without compromising integrity. Following this, normalization is performed to scale the data uniformly across different parameter ranges. This is especially important when dealing with diverse metrics such as temperature, pressure, and flow rates, which naturally operate on different scales. Normalization ensures that no single variable disproportionately influences the analytical models, leading to more balanced and accurate predictions.
Finally, the data undergoes temporal alignment, which synchronizes time-series data collected from multiple sources. This step is essential to establish correct causality relationships and ensure that concurrent events are properly correlated. Without proper alignment, the predictive models could misinterpret the sequence and impact of operational variables. Through these comprehensive pre-processing steps, the system ensures that the data fed into the AI/ML prediction module is clean, consistent, and properly structured - forming a solid foundation for accurate and actionable analysis.
AI/ML Prediction Module
The figure 1 shows AI/ML Prediction Module which represents the core intelligence of the system, employing sophisticated algorithms to analyze processed data and predict optimal setpoints for various process parameters. This module implements two primary machine learning algorithms:
Artificial Neural Network (ANN) Regression Algorithm
The implemented ANN regression algorithm follows a structured approach for predicting optimal process parameters:
Input Layer
The input layer processes the feature vector X = [x₁, x₂, ..., xₙ] where n represents the number of input features. These features include current process values, historical trend data, and product specifications. The input layer serves as the interface between the raw data and the neural network's computational components.
Hidden Layer(s)
Each hidden layer consists of multiple neurons that perform weighted summation operations followed by activation functions. For the jth neuron in the hidden layer, the computation is represented as:
z_j = ∑(i=1 to n) w_ij x_i + b_j
Where:
• z_j is the weighted sum for neuron j
• w_ij is the weight connecting input i to neuron j
• x_i is the value of input feature i
• b_j is the bias term for neuron j
The system implements multiple hidden layers to capture complex non-linear relationships between input parameters and desired outputs. The architecture employs a decreasing neuron count in successive layers to facilitate feature abstraction.
Activation Function
To introduce non-linearity into the model, each neuron applies an activation function to its weighted sum. The system primarily utilizes Rectified Linear Unit (ReLU) and Leaky ReLU activation functions:
ReLU: f(z) = max(0, z) Leaky ReLU: f(z) = max(αz, z) where α is a small constant (typically 0.01)
These activation functions provide computational efficiency while addressing the "dying ReLU" problem through the leak parameter in Leaky ReLU.
Output Layer
The output layer employs a linear activation function to generate continuous regression predictions:
y = ∑(j=1 to m) w_jz_j + b
Where:
• y is the predicted output value
• w_j is the weight assigned to hidden layer neuron j
• z_j is the activation of hidden layer neuron j
• b is the output bias term
• m is the number of neurons in the previous hidden layer
Loss Function
The system utilizes Mean Squared Error (MSE) as the primary loss function for training the neural network:
MSE = (1/N) ∑(i=1 to N) (y_i - ŷ_i)²
Where:
• N is the number of training samples
• y_i is the actual target value
• ŷ_i is the predicted value from the neural network
The optimization process minimizes this loss function using backpropagation and gradient descent techniques, adjusting network weights to improve prediction accuracy.
Extreme Gradient Boosting (XGBoost) Algorithm
In parallel with the ANN approach, the system implements XGBoost to enhance prediction accuracy through ensemble methods:
Sequential Tree Building
The Sequential Tree Building process is at the core of how the XGBoost algorithm operates, enabling it to deliver highly accurate and efficient predictions. XGBoost constructs decision trees in a sequential manner, with each new tree aiming to correct the mistakes made by the trees before it. This iterative refinement allows the model to progressively improve its performance with each step. The process begins with base learner initialization, where the first decision tree is trained on the available dataset. In regression tasks, this initial model typically predicts the average of the target variable, providing a baseline from which improvements can be made. While simple, this first step sets the stage for more precise adjustments in the following iterations.
Once the base model makes its predictions, the algorithm proceeds to error calculation. Here, it computes the residuals—the differences between the predicted values and the actual target values. These residuals highlight where the model's predictions have gone off track, effectively pinpointing the areas that require correction. The next phase is training the subsequent trees. Each new tree is specifically trained on these residual errors, with the objective of minimizing them. By focusing on the mistakes of previous trees, each additional tree incrementally refines the model's predictions, honing in on greater accuracy.
This iterative process continues, with each tree successively addressing the residual errors left by its predecessors. The building of trees proceeds until a predefined stopping criterion is met, such as reaching a maximum number of trees or achieving a minimal improvement threshold in performance metrics. Finally, the model generates its final prediction by combining the outputs from all the individual trees. This ensemble approach allows XGBoost to aggregate the strengths of multiple models, resulting in a powerful and robust predictive framework that consistently delivers high accuracy across a wide range of tasks.
Mathematical Formulation
The XGBoost model can be mathematically represented as an iterative process:
ŷ_i = ∑(k=1 to K) f_k(x_i)
Where:
• ŷ_i is the final predicted value for the ith data point
• K is the number of trees in the ensemble
• f_k(x_i) represents the prediction of the kth tree for the ith data point
The objective function in XGBoost consists of two parts: a loss function and a regularization term:
Obj = ∑(i=1 to n) l(y_i, ŷ_i) + ∑(k=1 to K) Ω(f_k)
Where:
• l(y_i, ŷ_i) is the loss function computing the difference between true and predicted values
• Ω(f_k) is the regularization term controlling model complexity
Regularization Implementation
The regularization term is defined as:
Ω(f) = γT + (λ/2)∑(j=1 to T) w_j²
Where:
• T is the number of leaves in the tree
• γ is a parameter controlling tree complexity
• λ penalizes the squared weight of the leaves
• w_j represents the score assigned to leaf j
Split Evaluation
When deciding how to split nodes, the algorithm computes information gain:
Gain = (G_L²/(H_L+λ)) + (G_R²/(H_R+λ)) - (G²/(H+λ)) - γ
Where:
• G_L, G_R are the sums of gradients in left and right child nodes
• H_L, H_R are the sums of Hessians in left and right child nodes
• G, H are the corresponding sums for the parent node
Hybrid Model Implementation
The system implements a hybrid approach that combines predictions from both ANN and XGBoost models using a weighted ensemble technique: Final Prediction = α × ANN_prediction + (1-α) × XGBoost_prediction Where α is a dynamic weighting factor determined based on historical performance of each model. This hybrid approach leverages the strengths of both algorithms while mitigating their individual weaknesses.
AI/ML Prediction Module
The AI/ML Prediction Module employs a structured optimization process to deliver precise and actionable recommendations for industrial operations. This module serves as the brain of the system, transforming historical and real-time data into optimized setpoints that drive improved efficiency and quality. The process begins with historical data analysis, where the system examines patterns and trends from past operational data. By identifying correlations between various input parameters and previously achieved optimal setpoints, the module builds a deep understanding of process behavior, laying the groundwork for accurate future predictions.
Next, the module performs feature importance calculation. Using advanced algorithms, it evaluates the relative significance of each input feature in influencing optimal outcomes. This step ensures that the most impactful variables are given appropriate weight in the prediction models, enabling more precise and reliable recommendations. For manufacturing environments that handle different product grades, the system incorporates grade-specific optimization. Separate prediction models are maintained and fine-tuned for each product grade, recognizing the unique requirements and characteristics associated with different production scenarios. This tailored approach enhances the flexibility and effectiveness of the system across diverse manufacturing lines.
To maintain peak performance over time, the module supports adaptive model updating. As new data flows into the system, the prediction models are continuously updated to reflect evolving process conditions and operational requirements. This adaptability ensures that the models remain current and effective even as production dynamics shift. An additional layer of reliability is provided through confidence interval generation. Alongside point predictions, the system outputs confidence intervals that quantify the uncertainty associated with each recommendation. This feature allows operators to gauge the reliability of the predictions and make informed decisions with a clear understanding of associated risks.
The ultimate output of this module consists of predicted optimal setpoints for key process parameters. These optimized values are then seamlessly utilized by the Model Predictive Control (MPC) Module, which implements the recommendations to enhance process control and drive operational excellence.
Model Predictive Control (MPC) Module
The MPC Module as shown in figure 2 serves as the control engine of the system, utilizing AI/ML predictions alongside real-time process values to calculate control variables that optimize process performance. This module represents a significant advancement over traditional PID controllers through its predictive capabilities and self-tuning functionality.
Figure 3 shows Auto-Tuning Algorithm. The MPC module implements an advanced auto-tuning algorithm based on the Ziegler-Nichols method to determine optimal controller parameters without manual intervention:
Bump Test Execution
The system performs a controlled bump test to perturb the process and analyze its response:
1. Step Change Introduction: A predetermined step change is applied to the control variable.
2. Response Measurement: The system records the process response to this perturbation.
3. Oscillation Analysis: The response is analyzed to identify key characteristics including overshoot, rise time, and oscillation period.
Parameter Calculation
Based on the bump test results, the system calculates essential tuning parameters:
1. Ultimate Gain (Ku): Calculated using the formula: Ku = 4 × CV Step / (PV Max - PV Min) Where CV Step is the magnitude of the control variable step change, and PV Max and PV Min are the maximum and minimum process values observed during the oscillatory response.
2. Ultimate Period (Pu): Measured as the time difference between consecutive peaks in the oscillatory response, in seconds.
Control Parameter Determination
Using the Ziegler-Nichols formulas, the system calculates optimal PID parameters:
1. Proportional Gain: Kp = 0.6 × Ku
2. Integral Gain: Ki = (2 × Kp) / Pu
3. Derivative Gain: Kd = Kp × Pu / 8
These parameters are then implemented in the control calculations to achieve optimal process performance.
MPC Optimization Process
The MPC Module executes a systematic optimization process for each control cycle:
Historical Data Retrieval
The system fetches the last 30 minutes of operational data from the database to establish context for optimization calculations. This data includes critical process values, their fluctuations, and previous control actions.
Real-time Data Acquisition
Current parameter values are retrieved from the server in real-time to provide instantaneous process status information:
• Process Value (PV): The current measured value of the controlled parameter (e.g., Steam Pressure = 5.6 bar)
• Prior Control Variable (CV0): The control output value from the previous cycle
Setpoint Determination
The system obtains the predicted optimal setpoint (SP) from the AI/ML Prediction Module based on current operating conditions, product specifications, and historical performance patterns (e.g., Predicted SP = 5.8 bar).
Control Calculation Execution
Using the auto-tuned PID parameters and the current process values, the system calculates the control variable components:
1. Error Calculation: error = SP - PV Example: error = 5.8 - 5.6 = 0.2
2. Proportional Term: P = Kp × error Example: P = 2.64 × 0.2 = 0.528
3. Integral Term: I = Ki × error / 60 Example: I = 21.12 × 0.2 / 60 = 0.0704
4. Derivative Term: D = 60 × Kd × error / Δt Example: D = 60 × 0.08 × 0.2 / 1 = 3.84
5. Control Variable Calculation: CV = CV0 + P + I + D Example: CV = 60 + 0.528 + 0.0704 + 3.84 = 64.43%
Output Implementation
The calculated control variable (CV) is implemented as the MPC Output, adjusting the physical process to achieve the desired setpoint. This output typically controls valve positions, motor speeds, heating elements, or other actuators that influence the process parameter.
Multi-variable Optimization
A key advantage of the MPC Module is its ability to handle multiple interrelated variables simultaneously:
Constraint Handling
The system implements sophisticated constraint handling mechanisms that respect physical limitations and operational boundaries:
1. Hard Constraints: Absolute limits that cannot be violated under any circumstances (e.g., maximum pressure ratings, temperature limits).
2. Soft Constraints: Preferred operating ranges that can be temporarily exceeded if necessary to achieve overall process stability.
Interaction Modeling
The MPC algorithm accounts for interactions between different process variables through a dynamic interaction matrix that quantifies the impact of each control variable on multiple process parameters.
Priority-based Optimization
When conflicts arise between different control objectives, the system implements a priority-based optimization approach:
1. Safety Constraints: Highest priority, never compromised
2. Product Quality Parameters: Second-highest priority
3. Resource Efficiency: Third-highest priority
4. Operational Stability: Baseline requirement
This prioritization ensures that the most critical process objectives are always maintained while optimizing secondary parameters when possible.
Performance Monitoring Module
The Performance Monitoring Module provides comprehensive evaluation of system efficiency, process stability, and control effectiveness. This module implements sophisticated statistical methods to calculate key performance indicators and presents them in an intuitive format for assessment.
Performance Metrics Calculation
The module calculates several critical performance metrics:
Statistical Process Measures
1. Actual Value: The measured output parameter value (e.g., 18.39%)
2. Set Point (SP): Predicted optimal target value (e.g., 18%)
3. Mean Value: Average of the output parameter over a specified time period (e.g., 18.35%)
4. Standard Deviation (Std): Measure of process variability calculated as the square root of variance (e.g., 0.76)
Control Limits Determination
Based on statistical analysis, the module calculates process control limits:
1. Lower Control Limit (LCL): Minimum acceptable value, typically calculated as: LCL = Mean - k × Std Where k is a configuration parameter (typically 1 for tight control) Example: LCL = 18.35 - 0.76 = 17.59
2. Upper Control Limit (UCL): Maximum acceptable value, typically calculated as: UCL = Mean + k × Std Example: UCL = 18.35 + 0.76 = 19.11
Performance Percentage
The module quantifies overall system performance through a composite metric:
Performance (%) = 100 × (1 - |Actual - SP| / SP) × (1 - Std / Mean) × InRangePercentage
Where InRangePercentage represents the proportion of time the process value remains within control limits.
Trend Analysis
The Performance Monitoring Module implements trend analysis capabilities to identify patterns and potential issues:
Short-term Trends
Analyzing data over minutes to hours to detect immediate process deviations or control issues that require attention.
Medium-term Trends
Evaluating data over shifts or days to identify operational patterns related to production schedules, raw material changes, or environmental factors.
Long-term Trends
Examining data over weeks to months to recognize gradual performance degradation, seasonal effects, or maintenance requirements.
Alarm Management
The module incorporates an intelligent alarm management system that prioritizes alerts based on severity, process impact, and resolution urgency:
1. Critical Alarms: Immediate attention required; potential safety or critical quality issues
2. Warning Alarms: Prompt attention advised; process deviation outside normal operating parameters
3. Advisory Notifications: Informational alerts indicating potential optimization opportunities
User Interface Module
The User Interface Module as shown in figure 4 and 5 provides a comprehensive visual representation of system operations, facilitating interaction, monitoring, and analysis. The interface is structured into multiple sections, each serving specific functional purposes:
Input Data Source Section
This section allows users to configure data source connections and displays current process parameters:
Data Source Selection
Users can select from available data sources:
• PI Server
• OPC Server
• PLC
• Other custom sources
Parameter Display
Real-time values for all process parameters are displayed in categorized panels:
• Air Parameters (Temperature, etc.)
• Water Parameters (Flow Rate, Pressure, Temperature)
• Steam Parameters (Flow Rate, Pressure, Temperature)
• Product Parameters (Flow Rate, Temperature, Grade, Batch, Blend)
AI/ML Prediction Section
This section visualizes the AI/ML prediction results and basis:
Predicted Setpoints
Displays optimal setpoints predicted by the AI/ML algorithms for key process parameters:
• Steam Pressure
• Water Temperature
• Product Temperature
Prediction Basis
Shows the contextual information used for prediction:
• Product Grade
• Batch Information
• Blend Specifications
Model Predictive Control Section
This section provides detailed information about the MPC calculations and adjustments:
Comparison Display
Shows a side-by-side comparison of:
• Actual Values (PV)
• Predicted Setpoints (SP)
• Calculated Error (SP - PV)
Control Variable Display
Presents detailed information about control calculations:
• Proportional Term (P)
• Integral Term (I)
• Derivative Term (D)
• Combined Control Variable (CV)
Adjustment Visualization
Graphically represents the control adjustments being applied to different process parameters.

Output Section
This section displays the final controlled parameters and process outcomes:

Controlled Parameters
Shows the current values of all controlled parameters:
• Moisture Output
• Water Parameters
• Steam Parameters
• Product Parameters
Process Performance
Provides real-time indicators of process performance and stability.
Performance Metrics Section
This section presents comprehensive performance evaluation metrics:
Statistical Metrics
Displays calculated statistical measures:
• Actual Value
• Set Point (SP)
• Mean Value
• Standard Deviation (Std)
Control Limits
Shows the calculated control boundaries:
• Lower Control Limit (LCL)
• Upper Control Limit (UCL)
Performance Indicator
Presents the calculated overall performance percentage along with trend information.
System Operation Flow
The complete system operates through a cyclical process that continuously optimizes industrial operations:
Initialization Phase
1. System configuration is loaded, establishing connections to data sources
2. Historical data is retrieved to establish baseline operation patterns
3. Initial AI/ML models are loaded or trained if no pre-existing models are available
4. Auto-tuning is performed to determine initial PID parameters
Operational Cycle
1. Data Collection: Real-time data is gathered from configured sources
2. Data Pre-processing: Collected data is cleaned and prepared for analysis
3. AI/ML Prediction: Optimal setpoints are predicted based on current conditions
4. MPC Optimization: Control variables are calculated based on predicted setpoints and actual values
5. Control Implementation: Calculated control outputs are applied to the physical process
6. Performance Evaluation: System performance is measured and evaluated
7. Model Updating: AI/ML models are periodically updated based on new operational data
This cycle repeats continuously, with each iteration further refining control parameters and predictions to achieve optimal process performance.
Implementation Benefits
The implementation of this AI-powered MPC system provides numerous advantages over traditional control systems:
Operational Improvements
1. Reduced Variability: The system maintains tighter control over process parameters, reducing product quality variations
2. Increased Efficiency: Optimized operation reduces energy consumption and raw material usage
3. Higher Throughput: Improved process stability allows for operation closer to design capacity
Economic Benefits
1. Lower Operating Costs: Reduced resource consumption translates to direct cost savings
2. Decreased Maintenance Requirements: Smoother operation reduces equipment wear and tear
3. Improved Product Quality: Consistent quality reduces rework and waste
Organizational Advantages
1. Reduced Operator Workload: Elimination of manual setpoint adjustments frees operators for higher-value tasks
2. Knowledge Retention: The system captures and implements best practices automatically
3. Data-Driven Decision Making: Performance metrics provide objective bases for operational decisions
The present invention represents a significant advancement in industrial process control technology, leveraging artificial intelligence and machine learning to implement a self-learning Model Predictive Control system. By eliminating the need for conventional PID controllers and operator intervention, the system achieves autonomous optimization of industrial processes, resulting in improved efficiency, reduced resource consumption, and enhanced product quality consistency. The comprehensive architecture encompassing data collection, AI/ML prediction, MPC optimization, and performance monitoring provides a complete solution for modern industrial control challenges. , Claims:We claim,
1. A method for Industrial Non-Linear Model process control and real-time optimization with Artificial Intelligence and machine learning comprising of:
a. Data collection module configured to gather real-time and historical data from multiple industrial sources and ensures seamless integration with diverse data streams, providing a unified platform for efficient monitoring, analysis, and decision-making across industrial environments;
b. Artificial Intelligence and Machine Learning (AI/ML) prediction module configured to analyze real-time and historical data to predict optimal setpoints for process parameters using at least one machine learning algorithm;
c. Advanced Model predictive control module configured to calculate control variables based on predicted setpoints and actual process values;
d. Performance monitoring module configured to statistical methods to calculate key performance indicators and evaluate system efficiency through real-time metrics; and
e. User Interface Module provides a comprehensive visual representation of system operations, facilitating interaction, monitoring, and analysis;
wherein the said model eliminates the need for physical Proportional-Integral-Derivative controllers by implementing a self-learning control algorithm which operates autonomously without manual intervention.
2. The method as claimed in claim 1, wherein the said data collection module interfaces with PI Servers to access historical trends and real-time sensor data, OPC servers as the standard communication bridge between industrial hardware devices and software applications, and PLCs monitor inputs and execute programmed logic to control outputs; and said organizes collected data into logical categories based on process air parameters, water parameters, steam parameters, and product parameters.
3. The method as claimed in claim 1, wherein the said AI/ML prediction module is a hybrid approach of Artificial Neural Network and Extreme Gradient Boosting algorithms using a weighted ensemble technique.
4. The method as claimed in claim 1, wherein the said advanced model predictive control module implements an auto-tuning algorithm based on the Ziegler-Nichols method to determine optimal control parameters without manual intervention.
5. The method as claimed in claim 1, wherein the said performance monitoring module calculates statistical process measures including actual value, set point, mean value, and standard deviation, and determines control limits.
6. The method as claimed in claim 1, wherein the said performance monitoring module incorporates an intelligent alarm management system comprises Critical Alarm, Warning Alarms and Advisory Notifications.
7. The method as claimed in claim 1, wherein said process initiated with model configuration, establishing connections to data sources, Historical data is retrieved to establish baseline operation patterns, Initial AI/ML models are loaded or trained, and Auto-tuning is performed to determine initial PID parameters.
Dated 20th Aug, 2025

Chothani Pritibahen Bipinbhai
Reg. No.: IN/PA-3148
For and on behalf of the applicant