An algorithm based on continuous measurement of multiphase flows of oil well production has been designed to improve the efficiency of the technical control of oil production processes in the field. Separation-free, non-contact measurement of multiphase flows of oil well products allows increasing the efficiency of managing oil production processes in the field. Monitoring the current density using radioisotope measuring transducers (RMTs) allows obtaining information about the structure of the flow in the form of the distribution of gas inclusions and the speed of movement of liquid and gas in a two-phase flow. Fluid velocity measurement is based on digital processing of RMT signals, applying a continuous or discrete undecimated wavelet transform to them, and assessing the cross-correlation of wavelet coefficients in individual subspaces of the wavelet decomposition. The cross-correlation coefficients of two RMT signals located at a base distance, calculated in the subspaces of the wavelet decomposition, characterize the speed of movement of gas bubbles of different sizes in a vertical pipe. The measurement assumes that the velocity of the liquid phase of the oil flow in a vertical pipe mainly corresponds to the velocity of small bubbles. This speed should be determined by the maximum cross-correlation of wavelet coefficients in the corresponding decomposition subspace. Computer modeling made it possible to evaluate the characteristics of the algorithm for controlling the speed of liquid movement in the gas–liquid flow of oil well products and determine the mass flow rate of the liquid and the relative value of the gas content. The implementation of the algorithm in a multi-channel version of the device allows monitoring an entire cluster of wells in the field.

The control of large-scale industrial systems has several criteria, such as ensuring high productivity, low production costs and the lowest possible environmental impact. These criteria must be established for all subsystems of the large-scale system. This study is devoted to the development of a hierarchical control system that meets several of these criteria and allows for the separate optimization of each subsystem. Multicriteria optimization is based on the processing of data characterizing production processes, which makes it possible to organize a multidimensional statistical control process. Using neural networks to model the technological processes of subsystems and the method of dynamic principal component analysis (DPCA) to reduce the dimensionality of control problems allows us to find more efficient solutions. Using the example of a two-level hierarchy, we showed a variant of the connection between two subsystems by parameters.

The efficient operation of industrial steam boilers in a combined heat and power plant is essential for improving efficiency and reducing operating costs in energy-intensive industrial processes. Industrial steam boilers play a central role in supplying steam energy to various industrial processes. The efficiency of steam boiler operation directly influences the fuel consumption, production cost and environmental performance. Industrial steam boilers exhibit multivariable, time-varying, and nonlinear behavior, which makes it difficult to develop an accurate first-principles model. Accordingly, this research develops an integrated data-driven MPC-MCO framework for multi-criteria optimization (MCO) of steam boilers. The ANN model is used to capture the steam boiler’s nonlinear characteristics. Subsequently, the LSTM model is employed to forecast future steam demand using historical operational data. The forecasts are integrated into the MPC framework to determine control actions for the steam boiler. A multi-criteria optimization layer is then added to identify Pareto optimal operating points by considering boiler efficiency and steam productivity. The proposed framework improves boiler efficiency by 3% and reduces fuel consumption by 4%. The proposed methodology, therefore, integrates the capabilities of neural network modeling, forecasting, predictive control and Pareto optimization to improve the efficient operation of steam boilers.