Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.

Multi-objective Optimization of Industrial Purified Terephthalic Acid Oxidation Process

Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a semi-empirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithra applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.

Dynamic Simulation and Analysis of Industrial Purified Terephthalic Acid Solvent Dehydration Process

Dynamic model for dehydration process of industrial purified terephthalic acid solvent is investigated to understand and characterize the process.A temperature differential expression is presented,which ensures the equation to convergence and short computation time.The model is used to study the dynamic behavior of an azeotropic distillation column separating acetic acid and water using n-butyl acetate as the entrainer.Responses of the column to feed flow and aqueous reflux flow are simulated.The movement of temperature front is also simulated.The comparison between simulation and industrial values shows that the model and algorithm are effective.On the basis of simulation and analysis,control strategy,online optimization and so on can be implemented effectively in dehydration process of purified terephthalic acid solvent.

Research and Implementation of Decreasing the Acetic Acid Consumption in Purified Terephthalic Acid Solvent System

Decreasing the acetic acid consumption in purified terephthalic acid （PTA） solvent system has become a hot issue with common concern. In accordance with the technical features, the electrical conductivity is in direct proportion to the acetic acid content. General regression neural network （GRNN） is used to establish the model of electrical conductivity on the basis of mechanism analysis, and then particle swarm optimization （PSO） algorithm with the improvement of inertia weight and population diversity is proposed to regulate the operating conditions. Thus, the method of decreasing the acid loss is derived and applied to PTA solvent system in a chemical plant. Cases studies show that the precision of modeling and optimization are higher. The results also provide the optimal operating conditions, which decrease the cost and improve the profit.

Purified terephthalic acid wastewater biodegradation and toxicity

The biodegradation and toxicity of the purified terephthalic acid（PTA） processing wastewater was researched at NJYZ pilot with the fusant strain Fhhh in the carrier activated sludge process（ CASP）. Sludge loading rate（SLR） for Fhhh to COD of the wastewater was 1.09 d^-1 and to PTA in the wastewater was 0.29 d^-1. The results of bioassay at the pilot and calculation with software Ebis3 showed that the 48h-LC50 （median lethal concentration） to Daphnia magna for the PTA concentration in the wastewater was only 1/10 of that for the chemical PTA. There were .5 kinds of benzoate pollutants and their toxicities existing in the wastewater at least. The toxicity parameter value of the pure chemical PTA cannot be used to predicate the PTA wastewater toxicity. The toxicity of the NJYZ PTA wastewater will be discussed in detail in this paper.

Fuzzy Support Vector Regression Model of 4-CBA Concentration for Industrial PTA Oxidation Process

In the past few years, support vector machines (SVMs) have been applied to many fields, such as pattern recognition and data mining, etc. However there still exist some problems to be solved. One of them is that the SVM is very sensitive to outliers or noises because of over-fitting problem. In this paper, a fuzzy support vector regression (FSVR) method is presented to deal with this problem. Strategies based on k nearest neighbor (kNN) and support vector data description (SVDD) are adopted to set the fuzzy membership values of data points in FSVR.The proposed FSVR soft sensor models based on kNN and SVDD are employed to predict the concentration of 4-carboxy-benzaldehyde (4-CBA) in purified terephthalic acid (PTA) oxidation process. Simulation results indicate that the proposed method indeed reduces the effect of outliers and yields higher accuracy.

Predication of Fhhh potential in PTA wastewater treatment

Ebis is the intelligent environmental biotechnological informatics software developed for judging the effectiveness of the microorganism strain in the industrial wastewater treatment system(IWTS) at the optimal status. The parameter, as the objective function for the judgment, is the minimum reactor volume( V _ min ) calculated by Ebis for microorganism required in wastewater treatment. The rationality and the universality of Ebis were demonstrated in the domestic sewage treatment system(DSTS) with the data published in USA and China at first,then Fhhh strain's potential for treating the purified terephthalic acid(PTA) was proved. It suggests that Ebis would be useful and universal for predicating the technique effectiveness in both DSTS and IWTS.

Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network

To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks(AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method,the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification(EDAC) is adopted. Soft sensor using AHNN based on EDAC(EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid(PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.

4-CBA Soft Sensor Based on Fuzzy CMAC Neural Networks

Soft sensor is attractive in dealing with online product quality measurement by virtue of other easily measured variables. In AMOCO PTA (purified terephthalic acid) production process, the unavailability of real-time measurement of 4-CBA makes it impossible for timely adjustment and thereby influences the product quality and the plant economy benefit. In this paper, a kind of FCMAC (fuzzy cerebellar model articulation controller) method is presented to solve the online measurement problem. Different from the conventional CMAC (cerebellar model articulation controller) networks, which has inferior smoothing ability because of its table look-up based technology. Integrating fuzzy model into CMAC networks, it becomes more accurate in functional mapping without weakening its generalization ability. Numerical example and industrial application results show the method proposed here is satisfactory and feasible.