Fuzzy cognitive approach of a molecular distillation process (original) (raw)
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Adaptive Neuro-Fuzzy Inference Systems (ANFIS) modeling of reactive distillation process
ABSTRACT This work has been carried out to develop ANFIS models for the reactive distillation process used for the production of isopropyl alcohol from the hydration reaction of propylene. The data used for the development of the models were generated from the Aspen HYSYS system of the process that comprised two feed streams -the upper feed stream from where the less volatile feed, water, was fed and the lower feed stream from where the more volatile feed, propylene, was fed into the column. The hydration reaction of the process was a reversible type occurring in liquid phase in the reaction sections of the column. The ANFIS models were trained, tested and simulated with the aid of MATLAB. The inputs of the models were the reflux ratio and the reboiler duty while the outputs were the top segment and the bottom segment temperatures. The high fit values and the low means of absolute errors obtained respectively from the training and the testing of the ANFIS models developed for the top segment and the bottom segment of the reactive distillation column used for the production of isopropyl alcohol have revealed that the developed ANFIS models represented the reactive distillation process in a very good manner.
Distillation - Innovative Applications and Modeling, 2017
Distillation is the process most commonly used in industry to separate chemical mixtures; its applications range from cosmetic and pharmaceutical to petrochemical industries. The equipment required to perform the distillation process is known as distillation column. Since initial investment and maintenance costs for distillation columns are very high it is necessary to have an appropriate mathematical model that allows improving the comprehension of the column dynamics, especially its thermal behaviour, in order to enhance the control and safety of the process. This chapter presents a general panorama of the mathematical modelling of distillation columns, having as a specific case of study the comparison of a space-state non-linear model and a Takagi-Sugeno fuzzy model for a batch distillation column using a binary mixture (Ethanol-Water).
In order to control the drying temperature of the PET resin in the silo of the plastic injection molding machine, during the plastic injection process in the industries producing preforms for the manufacture of beverage bottles, care is taken in the ideal temperature regulation for the better performance in controlling the generation of Acetaldehyde (AA), which alters the taste of carbonated or non-carbonated drinks, providing a citrus nuance to the palate and questioning the quality of the packaged products The objective of this work is to develop a tool based on Fuzzy logic to support the control of the drying temperature of PET resin, allowing specialists to make the ideal temperature control decisions necessary to control the generation of Acetaldehyde (AA). For the development of the proposed Fuzzy inference model, we used the Matlab Fuzzy toolbox tool, where the input variables, the fuzzyfication rules and the output variable were implemented based on the data collected from the preform injection process. From the inference model, we obtained a more precise management of the variables that influence the generation of AA, estimating a reduction of $ 240,044.00 in annual costs in the production of preforms.
Desalination, 2008
Multiple-effect (ME) distillation was the first process used to desalt a significant amount of seawater. This process takes place in a series of effects (stages) and uses the principle of reducing the ambient pressure in the various stages in order of their arrangement. In this work, a fuzzy logic is used to evaluate the factors affecting the ME distillers. The fuzzy logic detection was performed to assess three rules; i.e., "Increase", "Decrease", or "No Change" in distillation system in Jordan. We considered the factors that affect the detection of yield. There are many factors affect the ME distillers include: top brine temperature (TBT), concentration factor (CF), seawater temperature (T SW ), seawater pH (pH SW ), seawater salinity (S SW ), scale formation (SF), and CO 2 release. The various characteristics for the case study was synthesized and converted into relative weights w.r.t. fuzzy set method. The fuzzy set analysis for the case study reveals increase as confirmed by the experimental data. The application of the fuzzy set methodology offers reasonable prediction and assessment for detecting yield in distillation system in Jordan.
Neuro-Fuzzy Modelling in Petrochemical Industry
1999
In the last few years, problems concerning with both air pollution and quality of products have gained a particular attention in industrial companies. A great interest in new technologies for the process of manufacturing optimisation and quality control has raised. Mathematical models for quality control are highly nonlinear and need very expensive and sophisticated instruments. Soft-Computing, an innovative approach for constructing computationally intelligent systems, has just come into the limelight. The quintessence of designing intelligent systems of this kind is Neuro-Fuzzy computing. In this paper a Neuro-Fuzzy prediction model for the quality control of benzene is proposed.
American Journal of Computational and Applied Mathematics, 2012
In the present paper we use principles of fuzzy logic to develop a general model representing several processes in a system's operation characterized by a degree of vagueness and/or uncertainty. For this, the main stages of the corresponding process are represented as fuzzy subsets of a set of linguistic labels characterizing the system's performance at each stage. We also introduce three alternative measures of a fuzzy system's effectiveness connected to our general model. These measures include the system's total possibilistic uncertainty, the Shannon's entropy properly modified for use in a fuzzy environment and the "centroid" method in which the coordinates of the center of mass of the graph of the membership function involved provide an alternative measure of the system's performance. The advantages and disadvantages of the above measures are discussed and a combined use of them is suggested for achieving a worthy of credit mathematical analysis of the corresponding situation. An application is also developed for the Mathematical Modelling process illustrating the use of our results in practice.
Fuzzy Logic Application in Process Modeling of Biodiesel Reactor
2013
The transesterification reaction is actually replacement of alcohol group from an ester by another alcohol. The reaction was carried out by varying different parameters, like amount of catalyst in reaction, ratio of methyl alcohol to oil, temperature and stirring on the reaction; to find the best conversion of oil to biodiesel. In this paper fuzzy logic is applied to the transesterification reaction studies and the result is compared with the experimental results.
Statistical analysis of the main parameters in the fuzzy inference process
Fuzzy Sets and Systems, 1999
As there are many possibilities to select the set of basic operators used in the fuzzy inference process, the search for the fuzzy operators that are most suitable for the different steps of a fuzzy system, their characterization and evaluation, can be included among the most important topics in the field of fuzzy logic. A better insight into the performances of the alternative operators would make it easier to develop a fuzzy application. In the present paper, the relevancy and relative importance of the operators involved in the fuzzy inference process are investigated by using a powerful statistical tool, the ANalysis Of the VAriance (ANOVA) . The results obtained show that the defuzzifier and the T-norm operator are the most relevant factors in the fuzzy inference process. Moreover, this statistical analysis is able to establish a classification of the defuzzifiers and T-norms, according to their intrinsic characteristics. The conclusions here obtained justify the present interest, observed in many current papers, in studying both operators [6, 22-24, 33, 64, 67]. Futhermore, our results are confirmed by some experiments dealing with a real control application.
Notes on Sugeno and Yasukawa's fuzzy modelling approach
2001
This paper investigates the Sugeno's and Yasukawa's qualitative fuzzy modelling approach. We propose some easily implementable solution for the unclear details of the original paper. These details are crucial conceming the method's performance.
Fuzzy Modelling with Linguistic Equations
In this report, different types of fuzzy models have been developed from linguistic equations models. Different shapes of membership functions were compared: triangular and trapezoidal membership functions as well as their non-linear modifications were used. ANFIS (adaptive neuro-fuzzy inference system) method for Takagi-Sugeno type models was used and clustering for Singleton fuzzy models. Also the different number of singleton values in singleton fuzzy models and by using fuzzy relations different amount of rules was compared. The data-based approaches are based on data from the Cooking Liquor Analyser CLA 2000. Linguistic equations (LE) work well for this data. For the test data, the performance of the real-valued LE model was the best although a better fitting accuracy with training data was obtained by constructing Takagi-Sugeno (TS) fuzzy models with the ANFIS method. There are also overfitting problems with the TS models. Easy configuration and robustness are the main benefits of the LE models. The fitting performance must be compared to the number of modelling parameters.