Application of adaptive approach in control system of evaporation process at sugar factory using static model (Science journal «Technical service of agriculture, forestry and transport systems», 2025)
The article discusses the control system of the juice evaporation process in evaporator units. A regression model of the evaporation process is analyzed, revealing that the model possesses a number of properties that limit its applicability for control tasks. Thepaper addresses the development of mathematical models of the sugar production evaporation process that reflect process dynamics and are based on material and energy balance equations.The main directions for improving the efficiency of the control system for the dynamic evaporation process are identified, allowing for optimization of juice evaporation modes. The key approaches to determining effective mathematical models that describe the juice evaporation process in a multi-effect evaporator are considered. The advantages and disadvantages of mathematical models used in the mathematical support system for optimizing the evaporator operation modes are outlined.Effective algorithms for estimating regression coefficients of the juice evaporation process indicators, described by static models, are reviewed. For this purpose, an accelerated identification algorithm is proposed and investigated. This algorithm differs from existing ones by its higher convergence rate and robustness in the presence of measurement errors, as confirmed during the development of sugar production process models.The application of this algorithm in constructing mathematical models of the sugar production process ensured an acceptable identification error (10%) within 20–30 cycles. The use of this algorithm, in combination with the developed adaptive control algorithm, proved highly effective in solving stabilization problems of output variables in the evaporator section, maintaining a control (stabilization) error not exceeding 10%.
Keywords: adaptive approach; static model; evaporation process; estimation; regression coefficient; identifier