Support Vector Machine-based Soft Sensors in the Isomerisation Process

Herceg, Srečko; Andrijić, Željka Ujević; Bolf, Nenad

doi:10.15255/cabeq.2020.1825

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…Potential input variables as well as output variables, measured using the process analyzers AI-004B (measuring of 2,3-DMB) and AIC-005A (measuring of 3-MP content), are shown in a process scheme of the deisohexanizer section of the process (Figure 5). The correlation analysis performed for the potentially influential variables omitted some variables as model inputs because they showed little correlation with the outputs [41]. FIC-020 (DIH column side product flow) was excluded for 2,3-DMB content model development, and FIC-020 and FIC-026 (DIH column bottom product flow) were excluded for 3-MP.…”

Section: Model Developmentmentioning

confidence: 99%

“…In the search for a period with emphasized process A common problem may also be that parts of data can be missed due to problems with measurement equipment (Figure 7). some variables as model inputs because they showed little correlation with the outputs [41]. FIC-020 (DIH column side product flow) was excluded for 2,3-DMB content model development, and FIC-020 and FIC-026 (DIH column bottom product flow) were excluded for 3-MP.…”

Section: Model Developmentmentioning

confidence: 99%

“…Because of its structure, i.e., the idea of a dynamic neural network that allows data to be manipulated in past time intervals, and other advantages, LSTM is a suitable technique for developing models for such a process. The achieved results were compared with the results of various types of other data-driven models developed for an isomerization process, such as MLP artificial neural networks, SVMs, and dynamic polynomial models, presented and published earlier [40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Herceg,

Ujević Andrijić,

Rimac

et al. 2023

Mathematics

Self Cite

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Dynamic neural networks (DNNs) are a type of artificial neural network (ANN) designed to work with sequential data where context in time is important. Unlike traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if the dynamic of a certain process is emphasized. They are commonly used in natural language processing, speech recognition, and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial isomerization process, it is crucial to measure the quality attributes that affect the octane number of gasoline. Process analyzers commonly used for this purpose are expensive and subject to failure. Therefore, to achieve continuous production in the event of a malfunction, mathematical models for estimating product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNNs), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM), and dynamic polynomial models. The obtained results are satisfactory, suggesting a good possibility of application.

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Section: Model Developmentmentioning

confidence: 99%

Section: Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Herceg,

Ujević Andrijić,

Rimac

et al. 2023

Mathematics

Self Cite

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“…Besides Inline Analyzers and Laboratory Analysis, Virtual Online analyzers (VOA) (Soft Sensors or Virtual Online Analyzers) are used. Most of them are based on multivariate regression models [6,7] or artificial intelligence techniques [8,9,10].…”

Section: Literature Review and Identification Of Process Featuresmentioning

confidence: 99%

A simulation modeling approach used in the crude oil refining process

Tugashova

Баженов

Abdyldaeva

et al. 2022

J. Phys.: Conf. Ser.

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Mathematical and simulation modeling methods are applied when defining parameters of effective operation of oil fractionation. There is a review of software packages of process modeling used for refining ventures in the study. Multisphere model of oil fractionation with a low capacity applied is offered in the paper. The designed model allows taking into account constructive peculiarities of the equipment of small capacity units. The flow diagram of the model calculation algorithm with the additional evaluator of petroleum products quality indices is given by the authors. A design matrix for a three-factor experiment involving a multisphere model is obtained. An orthogonal central composite design has been applied to improve the accuracy of the model and to consider a non-linear parameter link. The MatLab software was chosen as an enabler. The criterion was the energy cost of separation. Factors affecting the criterion are flow rate of superheated water steam, reflux rate, potential light fractions in the crude/feed. The adequate obtained regression quadratic dependency towards the initial values by Fisher’s criterion has been assessed. The average relative error was 0.21 percent. The obtained statistical model can be applied for solving an optimization problem by the criterion of power consumption within a variable fractional composition of the raw material. Quality indices of oil products (gasoline fraction and diesel oil) serve as constraints. The offered statistical model applied at oil fractionation low-power facilities promotes the efficiency of plant operation improvement and the efficiency of obtaining information about the quality of oil products.

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“…Support vector machines deploy supervised learning modules mainly for classification, regression analysis, and outlier detection. Successful applications include development of soft‐sensors for controlling refinery processes, [ 269 ] the control of reactive distillation in conjunction with model predictive control, [ 240 ] and neuro‐prosthesis. [ 270,271 ]…”

Section: Miscellaneous Ai‐based Process Control Technologiesmentioning

confidence: 99%

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

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Support Vector Machine-based Soft Sensors in the Isomerisation Process

Cited by 5 publications

References 15 publications

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

A simulation modeling approach used in the crude oil refining process

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

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