Products M 5

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

Multi-criteria optimization
Incorporates advanced optimization algorithms capable of identifying Pareto-optimal solutions, ensuring a balanced trade-off between multiple objectives – for example, maximizing efficiency while minimizing energy consumption. This enables operational teams to determine the most effective control parameters for systems and processes.
Advanced predictive models
Integrates various machine learning algorithms, including neural networks, decision trees, and linear regression, enabling accurate modeling of key performance indicators.
User-friendly interface
The dashboard visualizes Pareto-optimal solutions and the corresponding control parameters.
Customization
The module can be adapted to various industrial conditions and operational requirements.

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.
Optimal Decision-Making
Multi-objective optimization helps decision-makers identify the best balance between conflicting objectives such as cost, quality, and production speed. It provides a set of Pareto-optimal solutions instead of a single solution. This allows industries to select the most suitable operating condition based on their priorities and requirements.
Examples of use
Multi-objective Optimization of Blast Furnace Ironmaking
Multi-objective Optimization of Blast Furnace Ironmaking

Objective of the Blast Furnace Operation:To reduce iron from oxides and produce liquid iron of a specified composition.

Optimization Objective: To determine the most cost-effective and environmentally friendly operating conditions for the blast furnace while ensuring stable iron quality (Si and S content, temperature).

Optimization Criteria:To minimize coke consumption and energy costs for blast air and pulverized coal injection (PCI), while complying with constraints on iron composition, lining durability, and CO₂ emissions.

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Multi-objective Optimization of Coordinated Control in Distributed Energy Generation Systems
Multi-objective Optimization of Coordinated Control in Distributed Energy Generation Systems
  • Control Objective:Effective, stable, and reliable coordination of distributed energy sources.
  • Optimization Objective: To reduce costs, increase the efficiency and reliability of the system, and support informed decision-making through AI-driven optimization.
  • Optimization Criteria: To minimize operational costs (such as fuel and maintenance expenses), maximize overall energy efficiency, and reduce CO₂ and NOx emissions.
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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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Multi-objective Optimization of Fluidized Bed Furnace
Multi-objective Optimization of Fluidized Bed Furnace
  • Objective of the Fluidized Bed Furnace: To remove sulfur from the concentrate and prepare the material for further processing.
  • Optimization Objective: To identify economically optimal operating parameters for the fluidized bed furnace while maintaining chemical reaction efficiency.
  • Optimization Criteria: To minimize energy consumption while ensuring the specified composition of the off-gases (SO₂ and O₂) based on the sulfur content in the initial concentrate.
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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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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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