Multi-objective optimization and improvement of production efficiency

The proposed software solution determines optimal control parameters to achieve extreme values of conflicting objective functions. Using advanced modern algorithms and artificial intelligence methods, we support decision-making through Pareto frontier construction based on operational data. This approach reduces uncertainty and improves production efficiency while ensuring operation within permissible limits.

Multi-objective optimization and improvement of production efficiency

Objective

To identify optimal control parameters for industrial systems and processes in order to maximize efficiency and minimize resource consumption.

Key features

Application Areas

01
Energy Source Management
The objective is to improve performance and reduce costs while meeting electricity demand by identifying optimal control parameters for energy sources.
02
Multi-Link Robotic Manipulator Control
Through multi-objective optimization, highly efficient and optimized motion control can be achieved, enabling complex tasks to be performed with greater precision and productivity.
Expected Optimization Results
Up to 10% Improvement in Operational Efficiency
By identifying optimal control parameters, industrial processes can operate at peak performance. The solution enables operational teams to dynamically adjust parameters in response to changing external conditions or variations in input data.
Reduced Energy Consumption and Resource Usage
The optimization module minimizes resource and energy utilization, ensuring more sustainable and cost-effective production.
Examples of use
Advanced hierarchical multi-objective control for boiler-turbine systems
Advanced hierarchical multi-objective control for boiler-turbine systems

Our platform provides a sophisticated multi-objective optimization control system designed for high-performance industrial applications. By employing advanced control theory, we significantly improve the efficiency and response time of boiler-turbine systems, as demonstrated through our control strategy for a 160 MW boiler-turbine model.

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Control of a 7-DOF robotic manipulator based on multi-objective optimization (MOO)
Control of a 7-DOF robotic manipulator based on multi-objective optimization (MOO)

Our platform uses multi-objective optimization (MOO) methods to control a seven-degree-of-freedom (7DOF) robotic manipulator. This approach ensures precise end-effector positioning by balancing multiple objectives, including cost minimization and transition speed maximization.

With MOO, users achieve high efficiency and optimal control, performing complex tasks with increased accuracy and performance.

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Intelligent Control System (ICS) for the Technological Regimes of Ore Concentrate Roasting in Fluidized Bed Furnaces at Mining and Processing Enterprises
Intelligent Control System (ICS) for the Technological Regimes of Ore Concentrate Roasting in Fluidized Bed Furnaces at Mining and Processing Enterprises

A set of tasks is being addressed:

  • Optimization of ore concentrate roasting regimes to intensify oxidation reactions of particles before the material proceeds to subsequent processing.
  • Monitoring and stabilization of the overpressure.
  • Environmental improvement by reducing the concentration of harmful sulfur oxides in exhaust gases.
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Unlock the hidden efficiency of your boiler system
Unlock the hidden efficiency of your boiler system

In complex multi-boiler systems, it’s easy to assume that you’re already operating at optimal efficiency. The load is distributed, steam demand is met, and the system is stable.

But stability does not equal optimality.

Our software analyzes the entire boiler system as a single organism, rather than a collection of individual units. We take into account real operational data, efficiency behavior at partial loads, fuel consumption, and steam demand, then mathematically determine the truly optimal operating mode.

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