FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2998194017> ?p ?o ?g. }

• W2998194017 endingPage "756" @default.
• W2998194017 startingPage "751" @default.
• W2998194017 abstract "In the chemical industry is important to control the process in order to guarantee the quality and repeatability of the final product. Using sensors in the industrial plant allows a large volume of data to be captured regarding the process. These data can be used for modelling to better understanding and predict the properties of the product in the process. In this work, two types of Artificial Neural Networks (ANN) and the hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS) were used to predict the density of polystyrene along the styrene polymerization process. The dataset used was extracted from the batch of polymerization reactions performed in open-loop, manual control and closed-loop and monitored in each 5 seconds. The Feedforward and Elman ANN has coefficient of correlation (R) equal 94.2%. However, the best topology obtained to Feedforward ANN presents 2 hidden layers and error index RMSE (Root Mean Squared Error) equal to 2.69x10-2. The Elman ANN presents only 1 hidden layer and RMSE of 3.39x10-2. The ANFIS model, in turn, presented R equal to 91% and RMSE of 0.2123. Therefore, ANFIS model did not prove to be the most adequate for the prediction of the polystyrene density in the studied process.Polymerization process pose significant challenges to the industrial community as it is difficult to control with high nonlinearity behaviour and fast dynamic response. The monitoring and control of polymer processes guarantee to the final product the qualities required by the market. Muhammad and Aziz (2017), for instance, studied the production of low density polyethylene (LDPE) and presented a review of the control strategies developed for the LDPE process. The strategies presented were developed in tubular and autoclave reactors and highlights the importance of nonlinear control in polymerization process.The use of sensors to monitor production allows a large volume of process data to be collected. Therefore, it is necessary to construct mathematical models of prediction to interpret and correlate significant patterns, indispensable to assist in the management of decisions and risk analysis.The development of phenomenological models for polymerization is complex and requires deep knowledge about the processes involved in each step. Modelling using artificial intelligence is a strategy that can provide valuable information about the process and allows the construction of an intelligent model capable of predicting process response based on parameters provided. ANN and ANFIS are artificial intelligence tools that can be used to build predictive models. In supervised learning, the data are presented to the network and its main objective is to provide a model that correctly correlates the pairs inputs - outputs of the problems. The use of ANN and ANFIS to predict the density of the polystyrene produced in the process becomes attractive since it is a type of non-linear modelling.Jumari and Mohd-Yusof (2017) presents models to measure melt flow index (MFI) in industrial polypropylene loop reactors using first principle (FP) model and ANN model. The authors state that the prediction of the ANN model is more accurate compare to the MFI calculated by the FP model. Furthermore, the CPU time recorded that ANN model is much faster than FP model.This work aims to develop direct and recursive ANN and ANFIS models from a set of experimental data from a controlled styrene polymerization plant capable of predicting the density of the product satisfactorily." @default.
• W2998194017 created "2020-01-10" @default.
• W2998194017 creator A5052756924 @default.
• W2998194017 creator A5054335211 @default.
• W2998194017 creator A5068868329 @default.
• W2998194017 creator A5084737487 @default.
• W2998194017 date "2019-05-31" @default.
• W2998194017 modified "2023-09-23" @default.
• W2998194017 title "Development of Mathematical Model Based on Artificial Neural Network to Predict Density in Polymerization Process of Styrene" @default.
• W2998194017 doi "https://doi.org/10.3303/cet1974126" @default.
• W2998194017 hasPublicationYear "2019" @default.
• W2998194017 type Work @default.
• W2998194017 sameAs 2998194017 @default.
• W2998194017 citedByCount "1" @default.
• W2998194017 countsByYear W29981940172023 @default.
• W2998194017 crossrefType "journal-article" @default.
• W2998194017 hasAuthorship W2998194017A5052756924 @default.
• W2998194017 hasAuthorship W2998194017A5054335211 @default.
• W2998194017 hasAuthorship W2998194017A5068868329 @default.
• W2998194017 hasAuthorship W2998194017A5084737487 @default.
• W2998194017 hasConcept C105795698 @default.
• W2998194017 hasConcept C111919701 @default.
• W2998194017 hasConcept C119857082 @default.
• W2998194017 hasConcept C127413603 @default.
• W2998194017 hasConcept C133731056 @default.
• W2998194017 hasConcept C139945424 @default.
• W2998194017 hasConcept C154945302 @default.
• W2998194017 hasConcept C186060115 @default.
• W2998194017 hasConcept C186108316 @default.
• W2998194017 hasConcept C195975749 @default.
• W2998194017 hasConcept C2775924081 @default.
• W2998194017 hasConcept C33923547 @default.
• W2998194017 hasConcept C38858127 @default.
• W2998194017 hasConcept C41008148 @default.
• W2998194017 hasConcept C47446073 @default.
• W2998194017 hasConcept C47702885 @default.
• W2998194017 hasConcept C50644808 @default.
• W2998194017 hasConcept C58166 @default.
• W2998194017 hasConcept C86803240 @default.
• W2998194017 hasConcept C98045186 @default.
• W2998194017 hasConceptScore W2998194017C105795698 @default.
• W2998194017 hasConceptScore W2998194017C111919701 @default.
• W2998194017 hasConceptScore W2998194017C119857082 @default.
• W2998194017 hasConceptScore W2998194017C127413603 @default.
• W2998194017 hasConceptScore W2998194017C133731056 @default.
• W2998194017 hasConceptScore W2998194017C139945424 @default.
• W2998194017 hasConceptScore W2998194017C154945302 @default.
• W2998194017 hasConceptScore W2998194017C186060115 @default.
• W2998194017 hasConceptScore W2998194017C186108316 @default.
• W2998194017 hasConceptScore W2998194017C195975749 @default.
• W2998194017 hasConceptScore W2998194017C2775924081 @default.
• W2998194017 hasConceptScore W2998194017C33923547 @default.
• W2998194017 hasConceptScore W2998194017C38858127 @default.
• W2998194017 hasConceptScore W2998194017C41008148 @default.
• W2998194017 hasConceptScore W2998194017C47446073 @default.
• W2998194017 hasConceptScore W2998194017C47702885 @default.
• W2998194017 hasConceptScore W2998194017C50644808 @default.
• W2998194017 hasConceptScore W2998194017C58166 @default.
• W2998194017 hasConceptScore W2998194017C86803240 @default.
• W2998194017 hasConceptScore W2998194017C98045186 @default.
• W2998194017 hasLocation W29981940171 @default.
• W2998194017 hasOpenAccess W2998194017 @default.
• W2998194017 hasPrimaryLocation W29981940171 @default.
• W2998194017 hasRelatedWork W152168236 @default.
• W2998194017 hasRelatedWork W1541342382 @default.
• W2998194017 hasRelatedWork W1978577284 @default.
• W2998194017 hasRelatedWork W1988516878 @default.
• W2998194017 hasRelatedWork W2013547400 @default.
• W2998194017 hasRelatedWork W2017598763 @default.
• W2998194017 hasRelatedWork W2019703807 @default.
• W2998194017 hasRelatedWork W2024147069 @default.
• W2998194017 hasRelatedWork W2024928411 @default.
• W2998194017 hasRelatedWork W2026509834 @default.
• W2998194017 hasRelatedWork W2027121866 @default.
• W2998194017 hasRelatedWork W2045902970 @default.
• W2998194017 hasRelatedWork W2345520406 @default.
• W2998194017 hasRelatedWork W2734812807 @default.
• W2998194017 hasRelatedWork W2911449079 @default.
• W2998194017 hasRelatedWork W3005899311 @default.
• W2998194017 hasRelatedWork W3013623430 @default.
• W2998194017 hasRelatedWork W3048450848 @default.
• W2998194017 hasRelatedWork W3123644651 @default.
• W2998194017 hasRelatedWork W855566522 @default.
• W2998194017 hasVolume "74" @default.
• W2998194017 isParatext "false" @default.
• W2998194017 isRetracted "false" @default.
• W2998194017 magId "2998194017" @default.
• W2998194017 workType "article" @default.