Advance Process Control
Unlock your potential with Harvinno Digital’s Advance Process Controllers. Our real-time analytics and automation boost efficiency, cut costs, improve quality, reduce downtime, and increase throughput. Transform complexity into clarity with smarter, faster, reliable control—where precision meets performance.
Unified Control Algorithms Powered by Cutting-Edge Technology
PID Control works well for simple single-input-single-output (SISO) fast responding systems.
Cascade Control use for a system with a relatively slow (i.e. level) and faster (i.e. flow) dynamic response. The slower responding controller manipulates the fast-acting controller.
Adaptive Control automatically adjusts the controller characteristics to compensate for variations in system dynamics, ensuring that the system operates at an optimal level.
State Control transition the control of your process among various production states. This allows the APC system to automatically toggle parts of your process in real-time, ensuring maximum performance and efficiency.
Model Predictive Control (MPC) utilizes a system’s model to forecast its future behavior and resolves the optimization problem to determine optimal control actions. MPC can manage multivariable (MIMO) systems with input and output responses.
Fuzzy Control is an experiential approach that embeds the knowledge of human thinking. It does not need an accurate mathematical model, can work with imprecise inputs and handle nonlinearity.
Expert Systems uses rule-based logic and plant expertise to assist or automate decision-making in process control, particularly useful for troubleshooting and anomaly detection.
Neural Network Control employs artificial neural networks to model complex nonlinear processes and adaptively control systems where traditional models are insufficient.
Control Layers
A hierarchical approach
Advance Regulatory Control
Objective
Stabilizing base layer control
Example
Controlling the flotation cell levels, airflow and reagents to their respective setpoints, by manipulating the respective control unit
Supervisory Control Layer
Objective
Regulate mass and energy balance
Example
Regulating the mass pull in a flotation circuit. Controlling feed flow to the final concentrate flow ratio to a SP.
Optimization Control Layer
Objective
Grade, Recovery and Throughput optimization
Example
Regulating concentrate and tailings grades based on target objectives. This is achieved through estimating the mass balance setpoint and reagent dosing setpoints.