This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE₁ = 1.865, ISE₂ = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure.

This study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of process parameters under variable feed conditions. The method addresses ore composition fluctuations by integrating three components: Physical modeling of particle motion, regression analysis, and neural network-based prediction. The jig bed level and pulsation frequency are used as control variables, while the Cr₂O₃ content in the feed ($C_r$) is treated as a disturbance. A neural network predicts the Cr₂O₃ content in the concentrate ($C_c$) and in the tailings ($C_t$), representing chromite-rich and gangue fractions, respectively. The optimization is performed using a constrained Interior-Point algorithm. The model demonstrates high predictive accuracy, with a mean squared error (MSE) below 0.01. The proposed control algorithm reduces chromium losses in tailings from 7.5% to 5.5%, while improving concentrate quality by 3–6%. A real-time human–machine interface (HMI) was developed in SIMATIC WinCC for process visualization and control. The hybrid framework can be adapted to other mineral processing systems by adjusting the model structure and retraining the neural network on new ore datasets.

This study proposes an intelligent control algorithm for multiple-input multiple-output (MIMO) industrial processes. This algorithm is based on the integration of a digital twin (DT), model predictive control (MPC), a genetic algorithm (GA), and a neural network (NN). The developed architecture employs a hybrid MPC scheme incorporating an additional NN correction branch. The workflow includes input data pre-processing, operating point linearization and NN training, computation of the optimal control sequence over a receding horizon, closed-loop control and adaptation based on prediction error. This innovative hybrid control law uses a linear state-space model as the base predictor and a compact NN superstructure to compensate for unmodeled nonlinearities. The GA searches for the optimal sequence of control actions while respecting process constraints and ensuring stable use of the NN correction. The methodology was tested on a phosphoric acid purification process. Compared to baseline MPC, the proposed algorithm increased purification efficiency to $95.1\%$, reduced the integral tracking error by $11.4\%$, and decreased the control signal amplitude by 10–15%. Selecting the appropriate reagent supply and vacuum modes ensured stable operation despite fluctuations in the raw material. These results confirm the effectiveness of DT-based hybrid control in applications requiring precision, adaptability, and strict constraint compliance. The approach is scalable and can be applied to other continuous production systems within Industry 4.0 initiatives.

The article is devoted to the synthesis of a SMART-system for a complex industrial object control based on metaheuristic optimization algorithms and modern industrial equipment from Honeywell Company. There has been developed software for collecting industrial data, automated tuning of typical controllers of a MIMO industrial object based on such intelligent optimization algorithms as ant colony algorithm, grey wolf optimization, dragonfly algorithm and cuckoo search algorithm. These algorithms are used to minimize the developed new modified quality criteria of a MIMO industrial object. The results are integrated into the Honeywell Experion PKS distributed control system for technological process control in the oil and gas industry using a distillation column for purifying gas from impurities as an example. On the basis of the decoupling procedure, the problem of compensating for the influence of the MIMO system interconnections is solved. The paper substantiates the effectiveness of the implementation of the developed SMART-system for solving the problems of optimal complex technological production control in the oil and gas industry on the example of the real production process of the TengizChevroil enterprise.

The article presents the developed methodology of intellectualization of industrial production of the oil and gas industry of the Republic of Kazakhstan on the basis of intelligent optimization algorithms, which provides a solution to the problems of synthesis of standard regulators using modern microprocessor technology. As an example, the distributed control system CENTUM VP from Yokogawa is used. To formulate the intellectualization procedure, the task is to find the parameters of a typical controller that provide the extremum of the objective function (quality criterion), using intelligent algorithms of global search optimization. In particular, the need to synthesize a proportional-integral-differential regulator based on an evolutionary population algorithm that minimizes the selected quadratic integral quality score. As intelligent algorithms, it is also proposed to use global search engine optimization algorithms that provide a solution to the problem based on artificial intelligence methods. Мақалада заманауи Микропроцессорлық техниканы қолдана отырып, типтік реттегіштерді синтездеу мәселелерін шешуді қамтамасыз ететін зияткерлік оңтайландыру алгоритмдері негізінде Қазақстан Республикасының мұнай-газ саласының өнеркәсіптік өндірісін зияткерлендірудің қалыптасқан әдістемесі ұсынылған. Мысал ретінде Yokogawa компаниясының CENTUM VP таратылған басқару жүйесі қолданылды. Интеллектуализация процедурасын тұжырымдау үшін ғаламдық іздеу жүйесін оңтайландырудың ақылды алгоритмдерін қолдана отырып, мақсатты функцияның экстремумын (сапа өлшемі) қамтамасыз ететін типтік реттеушінің параметрлерін табу міндеті тұр. Атап айтқанда, таңдалған квадраттық интегралдық сапаны бағалауды азайтатын эволюциялық популяция алгоритміне негізделген пропорционалды интегралды дифференциалды реттегішті синтездеу қажеттілігі. Ақылды Алгоритмдер ретінде жасанды интеллект әдістеріне негізделген мәселені шешуді қамтамасыз ететін ғаламдық іздеу жүйесін оңтайландыру алгоритмдерін қолдану ұсынылады. В статье представлена сформированная методология интеллектуализации промышленного производства нефтегазовой отрасли Республики Казахстан на основе интеллектуальных оптимизационных алгоритмов, обеспечивающая решение задач синтеза типовых регуляторов с применением современной микропроцессорной техники. В качестве примера использована распределённая система управления CENTUM VP фирмы Yokogawa. Для формулировки процедуры интеллектуализации поставлена задача поиска параметров типового регулятора, которые обеспечивают экстремум целевой функции (критерия качества), используя интеллектуальные алгоритмы глобальной поисковой оптимизации. В частности, необходимость синтеза пропорционально-интегрально-дифференциального регулятора на основе эволюционного популяционного алгоритма, который минимизирует выбранную квадратичную интегральную оценку качества. В качестве интеллектуальных алгоритмов также предложено использовать алгоритмы глобальной поисковой оптимизации, обеспечивающие решение постановленной задачи на основе методов искусственного интеллекта.