Dynamic process modeling and hybrid intelligent control of ethylene copolymerization in gas phase catalytic fluidized bed reactors

BACKGROUND: Polyethylene (PE) is the most extensively consumed plastic in the world, and gas phase‐based processes are widely used for its production owing to their flexibility. The sole type of reactor that can produce PE in the gas phase is the fluidized bed reactor (FBR), and effective modeling and control of FBRs are of great importance for design, scale‐up and simulation studies. This paper discusses these issues and suggests a novel advanced control structure for these systems. RESULTS: A unified process modeling and control approach is introduced for ethylene copolymerization in FBRs. The results show that our previously developed two‐phase model is well confirmed using real industrial data and is exact enough to further develop different control strategies. It is also shown that, owing to high system nonlinearities, conventional controllers are not suitable for this system, so advanced controllers are needed. Melt flow index (MFI) and reactor temperature are chosen as vital variables, and intelligent controllers were able to sufficiently control them. Performance indicators show that advanced controllers have a superior performance in comparison with conventional controllers. CONCLUSION: Based on control performance indicators, the adaptive neuro‐fuzzy inference system (ANFIS) controller for MFI control and the hybrid ANFIS–proportional‐integral‐differential (PID) controller for temperature control perform better regarding disturbance rejection and setpoint tracking in comparison with conventional controllers. © 2019 Society of Chemical Industr

Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Preparing, Characterizing, On-Line Digital Image Processing of Residence Time Distribution and Modeling of Mechanical Properties of Nanocomposite Foams

The objectives of this research were to prepare, characterize and to study the effects of organoclay and extrusion variables on the physical, mechanical, structural, thermal and functional properties of tapioca starch (TS)/poly(lactic acid) (PLA) nanocomposite foams. On-line digital imaging processing was used to determine residence time distribution (RTD). Adaptive neuro-fuzzy inference system (ANFIS) was used to model the mechanical properties of nanocomposite foams. Four different organoclays (Cloisite 10A, 25A, 93A, 15A) were used to produce nanocomposite foams by melt-intercalation. The properties were characterized using Xray diffraction, scanning electron microscopy, differential scanning calorimetric, and Instron universal testing machine. The properties were influenced significantly with the addition of different organoclays. TS/PLA/Cloisite 30B nanocomposite foams, with four clay contents of 1, 3, 5, 7 wt%, were prepared by a melt-intercalation method. Among the four nanocomposites, 3 wt% clay content produced significantly different properties. Screw speed, screw configuration, die nozzle diameter and moisture content were varied to determine their effects on organoclay intercalation. These extrusion variables had significant effects on the properties of TS/PLA /Cloisite 10A nanocomposite foams due to the intercalation of organoclay. Multiple inputs single output (MISO) models were developed to predict mechanical properties of nanocomposite foams. Four individual ANFIS models were developed. All models preformed well with R2 values \u3e 0.71 and had very low root mean squared errors (RMSE). Effects of screw configurations and barrel temperatures on the RTD and MISO models were developed to predict mechanical properties. The influence of the extrusion variables had a significant effect on the mean residence time (MTR). On-line digital image processing (DIP) technique was developed to measure the RTD as compared to the colorimeter method. R2 showed a correlation of 0.88 of a* values from both methods. The influence of screw configuration and temperature on RTD were analyzed by the MRT and variance for both methods. Mixing screws and lower temperature resulted in higher MRT and variance for both methods

Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance

This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity

Real-time Condition Monitoring and Asset Management of Oil- Immersed Power Transformers

This research pioneers a comprehensive asset management methodology utilizing solely online dissolved gas analysis. Integrating advanced AI algorithms, the model was trained and rigorously tested on real-world data, demonstrating its efficacy in optimizing asset performance and reliability

Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems

Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by special devices. Methodology. The construction of a fuzzy neural network is carried out using networks based on adaptive neuro-fuzzy system ANFIS. The network generated using the Grid Partition algorithm without clustering and Subtractive Clustering. Results. The paper presents a comparative analysis of fuzzy neural networks of various architectures in terms of increasing the accuracy of reproducing the moisture content of transformer oil. For training and testing fuzzy neural networks, the results of continuous monitoring of the temperature of the upper and lower layers of transformer oil during two months of operation used. Considered twenty four variants of the architecture of ANFIS models, which differ in the membership functions, the number of terms of each input quantity, and the number of training cycles. The results of using the constructed fuzzy neural networks for reproducing the dynamics of moisture content of transformer oil during a month of operation of the transformer are presented. The reproducing accuracy was assessed using the root mean square error and the coefficient of determination. The test results indicate the sufficient adequacy of the proposed models. Consequently, the RMSE value for the network constructed using Grid Partition method was 0.49, and for the network built using the Subtractive Clustering method – 0.40509.Вступ. Одним з параметрів, що визначають стан ізоляції силових трансформаторів, є ступінь вологості целюлозної ізоляції та трансформаторного масла. Сучасні системи неперервного контролю трансформаторного обладнання мають можливість накопичувати дані, які можуть бути використані для відтворювання динаміки вологості ізоляції при зміненні теплового режиму трансформатора. Метою роботи є відтворення кривої вологості трансформаторного масла за результатами вимірювання температури верхніх і нижніх шарів масла без необхідності прямого вимірювання вологовмісту спеціальними пристроями. Методологія. Побудова нечіткої нейронної мережі здійснюється із використанням адаптивних нейро-нечітких систем виводу ANFIS. Генерування моделі виконано за методами Grid Partition та Subtractive Clustering. Результати. Наведено порівняльний аналіз моделей ANFIS різної архітектури з точки зору підвищення точності відтворення кривої вологовмісту трансформаторного масла за результатами контролю температури його верхніх та нижніх шарів. При навчанні та тестуванні моделей ANFIS використовувались результати неперервного контролю трансформаторного масла протягом двох місяців експлуатації. Розглянуто двадцять чотири варіанти архітектури моделей ANFIS, які відрізняються функціями приналежності, кількістю термів кожної вхідної величини та кількістю циклів навчання. Представлені результати використання побудованих моделей ANFIS для відтворення кривої динаміки вологості масла протягом місяця експлуатації трансформатора. Точність відтворення кривої вологості масла оцінювалась шляхом розрахунку кореневої середньоквадратичної помилки та коефіцієнта детермінації. Результати тестувань свідчать про достатню адекватність запропонованих моделей. Значення кореневої середньоквадратичної помилки для моделі, побудованої із використанням методу Grid Partition, становило 0,49, а для моделі, побудованої з використанням методу Subtractive Clustering – 0,40509

Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems

Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by special devices. Methodology. The construction of a fuzzy neural network is carried out using networks based on adaptive neuro-fuzzy system ANFIS. The network generated using the Grid Partition algorithm without clustering and Subtractive Clustering. Results. The paper presents a comparative analysis of fuzzy neural networks of various architectures in terms of increasing the accuracy of reproducing the moisture content of transformer oil. For training and testing fuzzy neural networks, the results of continuous monitoring of the temperature of the upper and lower layers of transformer oil during two months of operation used. Considered twenty four variants of the architecture of ANFIS models, which differ in the membership functions, the number of terms of each input quantity, and the number of training cycles. The results of using the constructed fuzzy neural networks for reproducing the dynamics of moisture content of transformer oil during a month of operation of the transformer are presented. The reproducing accuracy was assessed using the root mean square error and the coefficient of determination. The test results indicate the sufficient adequacy of the proposed models. Consequently, the RMSE value for the network constructed using Grid Partition method was 0.49, and for the network built using the Subtractive Clustering method – 0.40509.Вступ. Одним з параметрів, що визначають стан ізоляції силових трансформаторів, є ступінь вологості целюлозної ізоляції та трансформаторного масла. Сучасні системи неперервного контролю трансформаторного обладнання мають можливість накопичувати дані, які можуть бути використані для відтворювання динаміки вологості ізоляції при зміненні теплового режиму трансформатора. Метою роботи є відтворення кривої вологості трансформаторного масла за результатами вимірювання температури верхніх і нижніх шарів масла без необхідності прямого вимірювання вологовмісту спеціальними пристроями. Методологія. Побудова нечіткої нейронної мережі здійснюється із використанням адаптивних нейро-нечітких систем виводу ANFIS. Генерування моделі виконано за методами Grid Partition та Subtractive Clustering. Результати. Наведено порівняльний аналіз моделей ANFIS різної архітектури з точки зору підвищення точності відтворення кривої вологовмісту трансформаторного масла за результатами контролю температури його верхніх та нижніх шарів. При навчанні та тестуванні моделей ANFIS використовувались результати неперервного контролю трансформаторного масла протягом двох місяців експлуатації. Розглянуто двадцять чотири варіанти архітектури моделей ANFIS, які відрізняються функціями приналежності, кількістю термів кожної вхідної величини та кількістю циклів навчання. Представлені результати використання побудованих моделей ANFIS для відтворення кривої динаміки вологості масла протягом місяця експлуатації трансформатора. Точність відтворення кривої вологості масла оцінювалась шляхом розрахунку кореневої середньоквадратичної помилки та коефіцієнта детермінації. Результати тестувань свідчать про достатню адекватність запропонованих моделей. Значення кореневої середньоквадратичної помилки для моделі, побудованої із використанням методу Grid Partition, становило 0,49, а для моделі, побудованої з використанням методу Subtractive Clustering – 0,40509

Determination of compressive strength of perlite-containing slag-based geopolymers and its prediction using artificial neural network and regression-based methods

This study has two main objectives: (i) to investigate the parameters affecting the compressive strength (CS) of perlite-containing slag-based geopolymers and (ii) to predict the CS values obtained from experimental studies. In this regard, 540 cubic geopolymer samples incorporating different raw perlite powder (RPP) replacement ratios, different sodium hydroxide (NaOH) molarity, different curing time, and different curing temperatures for a total of 180 mixture groups were produced and their CS results were experimentally determined. Then conventional regression analysis (CRA), multivariate adaptive regression splines (MARS), and TreeNet methods, as well as artificial neural network (ANN) methods, were used to predict the CS results of geopolymers using this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), scatter index (SI) and Nash-Sutcliffe (NS) performance statistics were used to evaluate the CS prediction capabilities of the methods. As a result, it was determined that the optimum molarity, curing time, and curing temperature were 14 M, 24 h, and 110 celcius, respectively and 48 h of heat curing did not have a significant effect on increasing the CS of the geopolymers. The highest performances in regression-based models were obtained from the MARS method. However, the ANN method showed higher prediction performance than the regression-based methods. Considering the RMSE values, it was seen that the ANN method made improvements by 24.7, 2.1, and 13.7 %, respectively, compared to the MARS method for training, validation, and test sets

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Macromixing study for various designs of impellers in a stirred vessel

The effect of the impeller designs and impeller clearance level (C/T) on power consumption, mixing time and air entrainment point in a single liquid phase under turbulent conditions (Re > 104) were investigated. Different impeller designs including conventional and new designs, were used to consider both axial and radial flow impellers. The electric conductivity method, suspended motor system and observation method were employed to determine the mixing time, the power consumption and the air entrainment point, respectively. The reduction in the impeller clearance level form T/3 to T/6 resulted in a decrease in power number values for up-flow pumping impellers while it was increased for down-flow pumping. The same trend was observed for the mixing time results. Moreover, axial flow impellers and specially HE3 are preferable for higher agitation speeds due to the less air entrainment. The results verified that the axial flow impellers and specifically down-flow impellers are more efficient than the radial flow impellers. ANFIS-Fuzzy C–means (ANFIS–FcM) and nonlinear regression were used to develop models to predict the mixing time based on the energy dissipation rate and clearance. The results verified that the model predictions successfully fit the experimental mixing time data