Chemical reaction and diffusion dynamics

In this paper diffusion with chemical reaction was investigated. The concomitant advances in theory, measuring systems and computer simulation bring the new perspectives to the chemical reaction with diffusion studying. The chemical rate and diffusion rate parameters were considered. The obtained results shows effects the chemical reaction rate and diffusion rate. The obtained results demonstrate characterization of the dynamic and steady state conditions, transition between them and how it can be used to predict the stability of the system

Fault locator of an allyl chloride plant

Process safety analysis, which includes qualitative fault event identification, the relative frequency and event probability functions, as well as consequence analysis, was performed on an allye chloride plant. An event tree for fault diagnosis and cognitive reliability analysis, as well as a troubleshooting system, were developed. Fuzzy inductive reasoning illustrated the advantages compared to crisp inductive reasoning. A qualitative model forecast the future behavior of the system in the case of accident detection and then compared it with the actual measured data. A cognitive model including qualitative and quantitative information by fuzzy logic of the incident scenario was derived as a fault locator for an ally! chloride plant. The obtained results showed the successful application of cognitive dispersion modeling to process safety analysis. A fuzzy inductive reasoner illustrated good performance to discriminate between different types of malfunctions. This fault locator allowed risk analysis and the construction of a fault tolerant system. This study is the first report in the literature showing the cognitive reliability analysis method

Automated learning and control using speedup and neural networks

This paper studies complex dynamic neural network learning models. Backpropagation was used to train a neural network for dynamic simulation and control of a chemical stirred tank. The generalized delta rule algorithm was used to train the network minimizing the sum of squares of the residual. It was assumed that a historical database of plant inputs and outputs is available. A Pseudo Random Binary Sequence-PRBS was used as a disturbance. For training the database the 1% PRBS signal was superimposed upon its steady state value from SPEEDUP simulation. Once a trained neural network model was available, it then used in real time learning and pH control. The examined inverse and standard neural network models controllers achieved better performance than a conventional PI controller. Complex Internal Model Control - IMC achieved the best results in control and local stability. The obtained models in this paper improve noisy handling and reduce process variability. Some of these systems can be used for self - maintaining non-linear, multivariable models and day to day troubleshooting

Diffusional processes in the biomass conversion

Diffusional process with biochemical reaction was investigated. Fermentation for ethanol production with Saccharomces Cerevisia was carried out with Ca-alginate gel in the form of layer spherical beads in the anaerobic conditions. The kinetic parameter determination was achieved by fitting reaction progress curves to the experimental data. The diffusion coefficients determination was performed for experimental conditions. The obtained results shows effect the biochemical reaction rate and diffusion

Process plant knowledge based simulation and design

A many number of modelling and simulation systems have been developed to aid in process and product engineering. In this paper the knowledge based process plant simulation model was developed. On the model development side, the issues of knowledge representation in the form of systematic component composition, ontology, and interconnections were illustrated. As a case study a plant for starch sweet syrup production was used. The system approach permits the evaluation of feasibility and global plant integration, and a predicted behavior of the reaction systems. The obtained results of the this paper have shown the variety quality of syrups simulation for different products

Modelling of the substance transfer in the gas-liquid system with chemical reaction

In this paper substance transfer phenomena in the multistage reactive system were investigated.Substance transfer between liquid and gas phases in a distillation packed column with reactionesterification was examined. Efficiency of substance transfer were examined by HTU-Height of transferunits and volumetric substance transfer coefficients. The esterification reaction of the ethanol andacetic acid was examined. Based on derived mathematical model the total height of transfer units andindividual height of transfer units for the liquid and gas phases were determined. The investigation wasperformed by computer simulation and experiments. Several correlation models for HTU/NTU andsubstance transfer coefficients were studied. The correlations for component transfer were derived inquaternary systems ethanol-ethyl acetate-acetic acid -water

The use of artifical neural networks for parameter determination in biological systems

In this paper an algorithm was developed for DNA state simulation using hydrogen bonds between complementary pairs and stacking interactions between neighboring hase pairs. In the aim to investigate the information transfer phenomenon between oligonucleotides, the hybridization reaction was modeled as a noisy channel in which error-free transmission of information occurs between perfect Watson-Crick complements, and erros occur if non-Watson-Crick base pairs are presented. The chemical potential of compounds was derived and a new group contribution method for DNA activity was developed. This method was implemented by using an artificial neural network. Parameters of different kinds of interactions and size differences between the molecules were predicted. An algorithm for DNA information processing, which involved hybridization equilibrium constants, activity coefficients of chain association and association constants, was develped. Melting profiles and local map stability prediction for sequenced DNA were illustrated. The amount of information that can be transmitted without error is bounded by the capacity of the channel. The main result in this paper is a new evolutionary algorithm which includes an artificial neural network model for DNA parameter determination and DNA state processing. The results of the communication depend on the hybridization reaction and reassociation, which also determine the number of molecules that can be interpreted as results without error. The computed results for hybridization energy were in a good agreement with the experimental data of other authors

The use of artifical neural networks for parameter determination in biological systems

In this paper an algorithm was developed for DNA state simulation using hydrogen bonds between complementary pairs and stacking interactions between neighboring hase pairs. In the aim to investigate the information transfer phenomenon between oligonucleotides, the hybridization reaction was modeled as a noisy channel in which error-free transmission of information occurs between perfect Watson-Crick complements, and erros occur if non-Watson-Crick base pairs are presented. The chemical potential of compounds was derived and a new group contribution method for DNA activity was developed. This method was implemented by using an artificial neural network. Parameters of different kinds of interactions and size differences between the molecules were predicted. An algorithm for DNA information processing, which involved hybridization equilibrium constants, activity coefficients of chain association and association constants, was develped. Melting profiles and local map stability prediction for sequenced DNA were illustrated. The amount of information that can be transmitted without error is bounded by the capacity of the channel. The main result in this paper is a new evolutionary algorithm which includes an artificial neural network model for DNA parameter determination and DNA state processing. The results of the communication depend on the hybridization reaction and reassociation, which also determine the number of molecules that can be interpreted as results without error. The computed results for hybridization energy were in a good agreement with the experimental data of other authors