Journal of Chemical Engineering & Process Technology

2020 ◽  
Keyword(s):  
Chemical Engineering ◽  
Engineering Process ◽  
Process Technology

scholarly journals An Overview of Difficulties in Controlling Intensified Process

2007 ◽  
Vol 7 (1 & 2) ◽  
pp. 8
Author(s):  
Reza Barzin ◽  
Syamsul Rizal Abd Shukor ◽  
Abdul Latif Ahmad
Keyword(s):  
Chemical Engineering ◽  
Process Intensification ◽  
Dynamic Interaction ◽  
Engineering Industry ◽  
Chemical Plants ◽  
Control Loop ◽  
Process Technology ◽  
Unit Operations ◽  
Potential Problems ◽  
Production Objectives

Process intensification (PI) is currently one of the most significant trends in chemical engineering and process technology. PI is a strategy of making dramatic reductions in the size of unit operations within chemical plants, in order to achieve production objectives. PI technology is able to change dramatically the whole chemical engineering industry pathway to a faster, cleaner and safer industry. Nonetheless, PI technology will be handicapped if such system is not properly controlled. There are some foreseeable problems in order to control such processes for instance, dynamic interaction between components that make up a control loop, response time of the instrumentations, availability of proper sensor and etc. This paper offers an overview and discussion on identifying potential problems of controlling intensified systems.

scholarly journals Heat Capacities and Thermodynamic Properties of Hungchaoite and Mcallisterite

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4470
Author(s):  
Jiangtao Song ◽  
Fei Yuan ◽  
Long Li ◽  
Yafei Guo ◽  
Tianlong Deng
Keyword(s):  
Phase Transition ◽  
Thermodynamic Properties ◽  
Thermodynamic Functions ◽  
Process Design ◽  
Chemical Engineering ◽  
Heat Capacities ◽  
Salt Lakes ◽  
Engineering Process ◽  
Thermal Anomalies ◽  
First Time

The heat capacities on two minerals of hungchaoite (MgB4O7·9H2O, Hu) and mcallisterite (MgB6O10·7.5H2O, Mc) have been measured with a precision calorimeter at temperatures ranging from 306.15 to 355.15 K, experimentally. It was found that there are no phase transition and thermal anomalies, and the molar heat capacities against temperature for the minerals of hungchaoite and mcallisterite were fitted as C p , m , Hu   =   − 27019.23675 + 229.55286 T   −   0.63912 T   2   +   ( 5.95862   ×   10   − 4 )   T   3 and C p , mMc   =   − 9981.88552   +   84.10964 T   −   0.22685 T   2   +   ( 2.0593   ×   10   − 4 )   T   3 , respectively. The molar heat capacities and thermodynamic functions of (HT-H298.15), (ST-S298.15), and (GT-G298.15) at intervals of 1 K for the two minerals were obtained for the first time. These results are significant in order to understand the thermodynamic properties of those minerals existing in nature salt lakes, as well as applying them to the chemical engineering process design.

Design and Optimization of a Filter Based on Artificial Neural Network Applied to a Distillation Column

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
Jose S Torrecilla ◽  
Adela Fernández ◽  
Julian Garcia ◽  
Francisco Rodríguez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distillation Column ◽  
Chemical Engineering ◽  
Engineering Process ◽  
Design And Optimization ◽  
Noisy Signals ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Design And Application

This paper discusses the design and application of a filter based on an Artificial Neural Network (ANN) in a chemical engineering process. The design of a filter consists of adapting the algorithms that make up the filter to the process to be filtered. Taking into account that the ANN is able to model almost every type of chemical process, the design and application of a filter based on ANN was studied. In this work, every ANN used was based on Multilayer Perceptron (MLP). Bearing in mind that ANN should reproduce the process as accurately as possible, an optimisation of the ANN (training function and parameters) was carried out. A mathematical model of a reflux in the upper part of a distillation column was used to test the ANN filter. The ANN is able to filter noisy signals with a mean prediction error less than 2.5•10-3 %.

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