Application of fuzzy inference system to polypropylene business policy in a petrochemical plant in India

Dey, Sanghamitra; Jana, Dipak Kumar

doi:10.1016/j.jclepro.2015.09.120

Cited by 24 publications

(14 citation statements)

References 21 publications

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“…As compared to the previous work in this area of research like as Jana et al, [ 33,43 ] we observe that our ANN prediction is more effective than the other prediction in the PP business. It is due to the fact that in this current model, we get the minimum RMSE, MAE, and MAPE as compared to the other predictions in this business.…”

Section: Comparison With the Earlier Worksupporting

confidence: 44%

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Singh

Majumder

Bera

2022

Polymer Engineering & Sci

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The quality of polypropylene is one of the major components in the plastic industry. The quality of the polypropylene depends upon the melt flow index and the xylene solubility under the given condition such as hydrogen flow, donor flow, pressure, temperature, etc. This study investigates the use of artificial neural network (ANN) modeling for the prediction of the quality of polypropylene for petrochemical plants. This study proposes an ANN model to predict the “melt flow index (MFI) and xylene solubility” as the quality of polypropylene depends upon these two factors. Hydrogen (H2) flow, pressure, temperature, and donor flow are the controlling parameters for the MFI and xylene solubility. The study proposes a new approach for the selection of the best topology using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) multicriterion decision‐making (MCDM) process for the ANN technique. Experimental data are trained with three training algorithms each with four combinations of training functions and each with three combinations of numbers of neurons. In this study, the best model for ANN is found by the Levenberg–Marquardt backpropagation training algorithm with logarithmic sigmoid and hyperbolic tangent sigmoid transfer functions. The best topology for the ANN model for prediction is selected by using the TOPSIS method. Some sensitivity analyses are provided graphically to show the physical nature of the problem.

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Section: Comparison With the Earlier Worksupporting

confidence: 44%

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Singh

Majumder

Bera

2022

Polymer Engineering & Sci

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“…The application procedure of a FIS system consists of three steps: an input stage, a processing stage, and an output stage [15,25]:…”

Section: Fuzzy Inference System (Fis)mentioning

confidence: 99%

“…Unlike principal component analysis (PCA) and other multivariate statistical process monitoring (MSPM) methods [13,14], a fuzzy inference system (FIS) can integrate datadriven modelling and the priceless expert knowledge by the designation of membership functions and fuzzy rules [15]. Successful applications of it are also attributable to its superiority to manage uncertainty and computation for noisy and imprecise data.…”

Section: Introductionmentioning

confidence: 99%

A Quality Integrated Fuzzy Inference System for the Reliability Estimating of Fluorochemical Engineering Processes

Feng

Zhou

et al. 2021

Processes

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Hypertoxic materials make it critical to ensure the safety of the fluorochemical engineering processes. This mainly depends on the over maintenance or the manual operations due to the lack of precise models and mechanism knowledge. To quantify the deviations of the operating variables and the product quality from their target values at the same time and to overcome the measurement delay of the product quality, a novel quality integrated fuzzy inference system (QFIS) was proposed to estimate the reliability of the operation status as well as the product quality to enhance the performance of the safety monitoring system. To this end, a novel quality-weighted multivariate inverted normal loss function was proposed to quantify the deviation of the product quality from the target value to overcome the measurement delay. Vital safety process variables were identified according to the expert knowledge. Afterward, the quality loss and the vital variables were inputs to an elaborate fuzzy inference system to estimate the process reliability of the fluorochemical engineering processes. By integrating the abundant expert knowledge and a data-driven quality prediction model to design the fuzzy rules of QFIS, not only the operation reliability but also the product quality can be monitored on-line. Its superiority in estimating system reliability has been strongly proved by the application of a real fluorochemical engineering process located in East China. Moreover, the application of the Tennessee Eastman process also confirmed its generalization performance for other complicated black-box chemical processes.

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“…Fuzzy logic has found extensive applications in environmental management related issues (Dey and Jana 2016). In Chakraborty, et al (2016) fuzzy logic was applied based to the detection of Parkinson's disease while Lincy and John (2016), Dash and Dash (2016) applied fuzzy logic to stock trading decision making problems.…”

Section: The Fuzzy Logic: a General Overviewmentioning

confidence: 99%

Measuring flood resilience: a fuzzy logic approach

Oladokun

Proverbs

Lamond

2017

IJBPA

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Flood resilience is emerging as a major component of an integrated strategic approach to flood risk management. This approach recognizes that some flooding is inevitable and aligns with the concept of ‗living with water'. Flood resilience measurement has been recognized as key for making the business case for investments in resilient retrofits and adaptations and, could potentially be used to inform the design of new developments where there is a risk of flooding.The literature is however sparse on frameworks for quantifying or measuring the level of resilience of flood prone households. This study describes the development of a fuzzy logic based flood resilience measuring model, drawing on a synthesis of extant flood resilience and fuzzy logic literature. An abstraction of the flood resilience system followed by identification and characterisation of systems' variables and parameters were carried out. The resulting model was transformed into a fuzzy inference system (FIS) using three input factors; Inherent resilience (IR), Supportive Facilities (SF) and, Resident Capacity (RC). The resulting fuzzy inference system generates resilience index for households with a wide range of techno-economic and socio-environmental features. . It is concluded that the fuzzy logic based model provides a veritable tool for the measurement of flood resilience at the level of the individual property, and with the potential to be further developed for larger scale applications i.e. at the community or regional levels.

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Application of fuzzy inference system to polypropylene business policy in a petrochemical plant in India

Cited by 24 publications

References 21 publications

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

Prediction of polypropylene business strategy for a petrochemical plant using a technique for order preference by similarity to an ideal solution‐based artificial neural network

A Quality Integrated Fuzzy Inference System for the Reliability Estimating of Fluorochemical Engineering Processes

Measuring flood resilience: a fuzzy logic approach

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