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.




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.

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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A set of tasks is being addressed:
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.