The presented paper provides an overview of approaches to automation of the drying process in the fluidized bed mode. The technological process of drying potassium chloride in a fluidized bed apparatus at a potash plant is considered as an object of control. During the analysis of the control object, it was noted that, according to the generalized opinion of the technical staff of the production in question, standard control algorithms (PID controllers) are not effective enough in the task of automatic control of the thermal regime of the potassium chloride drying process. The control of the thermal regime in the production under consideration is carried out by the operational personnel in manual mode, by manipulating the flow rate of the drying agent. The need to simultaneously solve and take into account many different kinds of process control tasks in the drying compartment leads to the fact that the operator is physically unable to constantly ensure accurate maintenance of the thermal drying regime in accordance with the regulations in conditions of fluctuations in the consumption of wet crystallized, which reduces the quality of the drying process control and leads to overspending of fuel gas. Analysis of the conditions and results of the operation of the production in question showed that standard algorithms are not able to provide the required quality of process control. The use of more advanced methods and algorithms of automatic control is required. A review of literature sources shows that the solution to the existing problem can be found based on the use of algorithms that meet intelligent automatic control systems. One of the modern approaches to automation of complex, informationally weakly deterministic technological processes is the intellectualization of the control system. Intelligent control algorithms are considered to be built on the basis of models for the representation of expert knowledge. Such algorithms are able to maintain operability in conditions of non-stationarity of process variables and incompleteness of the observed information about its state." "The presented paper provides an overview of approaches to automation of the drying process in the fluidized bed mode. The technological process of drying potassium chloride in a fluidized bed apparatus at a potash plant is considered as an object of control. During the analysis of the control object, it was noted that, according to the generalized opinion of the technical staff of the production in question, standard control algorithms (PID controllers) are not effective enough in the task of automatic control of the thermal regime of the potassium chloride drying process. The control of the thermal regime in the production under consideration is carried out by the operational personnel in manual mode, by manipulating the flow rate of the drying agent. The need to simultaneously solve and take into account many different kinds of process control tasks in the drying compartment leads to the fact that the operator is physically unable to constantly ensure accurate maintenance of the thermal drying regime in accordance with the regulations in conditions of fluctuations in the consumption of wet crystallized, which reduces the quality of the drying process control and leads to overspending of fuel gas. Analysis of the conditions and results of the operation of the production in question showed that standard algorithms are not able to provide the required quality of process control. The use of more advanced methods and algorithms of automatic control is required. A review of literature sources shows that the solution to the existing problem can be found based on the use of algorithms that meet intelligent automatic control systems. One of the modern approaches to automation of complex, informationally weakly deterministic technological processes is the intellectualization of the control system. Intelligent control algorithms are considered to be built on the basis of models for the representation of expert knowledge. Such algorithms are able to maintain operability in conditions of non-stationarity of process variables and incompleteness of the observed information about its state.

Keywords: fluidized bed drying, potassium chloride, control, process automation, PID, intelligent control algorithmsfluidized bed drying, potassium chloride, control, process automation, PID, intelligent control algorithms

In the production of petroleum products, the study of IR absorption spectra is most often used to analyze the properties of a mixture. The priority of this method is due to the fact that the characteristics of the IR spectrum are directly related to the nature (structure and chemical composition) of the absorbing substance, and also depend on the aggregate state of the substance, temperature, pressure, etc. The unambiguity of the relationship between the molecular structure of a substance and its IR spectrum allows us to determine the composition of the mixture. For this purpose, calibration models should be built that connect the IR spectrum with the value of the quality indicators of petroleum products. The paper considers the methods of creating calibration models proposed by various authors, presented in well-known literary sources. To create calibration models in this paper, it is proposed to use the method of principal components and neural network modeling. Also, in order to increase the reliability of the automated control system for compounding motor fuels, it is proposed to use virtual analyzers (VA) of quality indicators of communication models, the quality indicator calculated from the calibration models of the flow analyzer with the corresponding technological variables of the compounding process. The output of the calibration models is also used for adjusting the VA.

Keywords: Oil refining, IR spectrometer, quality indicators of petroleum products, flow IR quality analyzer, calibration models of the analyzer, virtual quality analyzer

An approach to solving the problem associated with the calculation of the optimal parameters of the technological process in the field oil treatment plants is considered. The calculation algorithm is based on the dynamic programming method that implements the Bellman optimality principle with an additive optimality criterion. The generalized criterion of the problem is formed as a function of the sum of local reduced costs for the stages of oil treatment. The proposed method of solving the problem allows to predict the optimal parameters of the process regime and transfer them to the control system as tasks to operators and automatic controllers.

Keywords: field oil treatment, facility, technological regime, model, optimization, operative management

The method of managing the recipe of dry magnesia cement mixtures (DMCM) during their production, intended for cement slurries for cementing oil and gas wells, is characterized in that the recipe of the mixture is selected based on the requirements for the characteristics of the cement slurry and the quality of the cement stone, which are determined in the order in relation to the mining and geological conditions of a particular well. The method is based on solving multiobjective optimization problem in which as criteria protrude partial deflection characteristics of the resulting solution of the SMTS from predetermined values in order for the production batch. Generalized criterion problem is formed as an additive objective function of the weighted partial criteria.

Keywords: oil and gas wells, rheological characteristics of cement slurry, dry magnesia mixture, experiment planning, regression models of communication, optimization of mixture formulation, generalized criterion

Results of research on control object identification based on neural network processes modeling are given. Model of object with control system is represented with a dynamic neural network and regulator model. Regulator function is known. Neural network is trained on the data of control object operating. The resulting model simulate the behavior of the system and lets us find the system’s output, including outputs for periodic test influences. By the resulting complex frequency response we find the parameters of the channel. Observed objects represent technological processes with continuous production. We show an example of identification for laboratory control object channel.

Keywords: Object with control system, identification, neural network, modeling, complex frequency response, transfer function