ng-Koo Yeo

Optimal Operation of the Pressure Swing Adsorption (PSA) Process for CO2 Recovery

The operation of PSA (Pressure Swing Adsorption) processes is a highly nonlinear and challenging problem. We propose a systematic procedure to achieve the optimal operation of a PSA process. The model of the PSA process for CO2 separation and recovery is developed first and optimization is performed to identify optimal operating conditions based on the model. The effectiveness of the model developed is demonstrated by numerical simulations and experiments using CO2 and N2 gases and zeolite 13X. Breakthrough curves and temperature changes in the bed are computed from the model and the results are compared with those of experiments. The effects of the adsorption time and reflux ratio on the product purity and the recovery are identified through numerical simulations. The optimization problem is formulated based on nonlinear equations obtained from simulations. The optimal operating conditions identified are applied to experiments. The results show higher recovery of CO2 under optimal operating conditions.

Optimal design of a chemical heat pump using the 2-propanol/acetone/hydrogen system

Low-level thermal energy is upgraded by using the reversible reactions of 2-propanol dehydrogenation and acetone hydrogenation. A new design criterion for optimal operation is proposed using modeling and numerical simulation. Optimal values have been obtained for the reflux ratio and the number of trays of the distillation column for given operating conditions. Simulation results show that the enthalpy efficiency is affected by the reflux ratio, feed positions into the distillation column and reaction temperatures.

Conversion of CH4 and CO2 to Syngas and Higher Hydrocarbons Using Dielectric Barrier Discharge

The conversion of methane to syngas and other hydrocarbons in dielectric barrier discharge plasma under the presence of CO2 was investigated. Effects of the input voltage on the conversion of methane and CO2 and the ratio of syngas were analyzed experimentally. The results of numerical simulations showed good quantitative agreement with those of experiments.

Prediction of Air Pollutants by Using an Artificial Neural Network

The purpose of this study is to predict the amount of primary air pollution substances in Seoul, Korea. An artificial neural network (ANN) was used as a prediction method. The ANN with three layers is learned with past data, and the concentrations of air pollutants are predicted based on the pre-learned weights. The error back propagation method that has a powerful application to various fields was adopted as the learning rule. The concentrations of air pollutants from one to six hours in the future were predicted with the ANN. To verify the performance of the prediction method used in the present study, the predicted concentrations of air pollutants were compared with the measured data. From the comparison, it was found that the prediction method based on the ANN gives an acceptable accuracy for the limited prediction horizon.

FUZZY MODEL PREDICTIVE CONTROL OF NONLINEAR pH PROCESS

A new fuzzy model-based predictive control scheme was developed to control a nonlinear pH process. The control scheme is based on the Takagi-Sugeno type fuzzy model of the process being controlled. In the present fuzzy model predictive control method, the process model maintains a linear representation of the conclusion parts of fuzzy rules. Therefore, it has a significant advantage over other types of models in the sense that nonlinear processes can be handled effectively by embedding the linear characteristic. The fuzzy model of the pH process to be controlled was constructed and used in the predictive control algorithm. Results of computer simulations and experiments demonstrated the effectiveness of the present control method.

MODELING AND SIMULATION OF ENERGY DISTRIBUTION SYSTEMS IN A PETROCHEMICAL PLANT

A systematic method for analysis and design of plant-wide energy distribution systems is proposed to minimize the net cost of providing energy to the plant. The method is based on the steady-state modeling and simulation of steam generation process and steam distribution network. Modeling of steam generation process and steam distribution network were performed based on actual plant operation data. Heuristic operational knowledges are incorporated in the modeling of steam distribution network. Newton’s iteration method and a simple linear programming algorithm were employed in the simulation. The letdown amount from superheated high-pressure steam (SS) header and the amount of SS produced at the boiler showed good agreement with those of actual operational data.

Analysis of catalytic reaction systems under microwaves to save energy

Effects of microwaves on catalytic reaction systems are analyzed theoretically in this work. Use of microwaves is encouraged to save energy. The effects of microwave heating are analyzed theoretically by assuming that the catalyst pellet is homogeneous. The temperature and concentration profiles within the catalyst pellet were obtained by numerical simulations for the cases of microwave heating and conventional heating. In the modeling the catalyst pellet is regarded as a continuum. When a chemical reaction was conducted in a heterogeneous medium with microwave heating, the reaction rate and the yield were found to be increased compared to conventional heating under the same reaction conditions. This is due to hot spots generated by selective heating of the catalyst pellet, resulting in an increased reaction rate.

Modeling and simulation of a sulfolane extraction process

Modeling and simulation for a sulfolane extraction process were performed. The process investigated in the present work consists of two tray columns, one of which is used for extraction and the other one for washing. The modified θ-method was employed for the modeling of the process. The computation of the equilibrium state of a two-phase system is one of the key issues in the modeling and simulation of extraction processes. In the present work the UNIFAC method was used for the computation of related thermodynamic properties and equilibrium states. Results of simulations showed good agreement with actual plant operation data.

Identification of kinetics of direct esterification reactions for PET synthesis based on a genetic algorithm

In this study we propose a method to identify the kinetics of direction esterification reactions for polyethylene terephthalate (PET) based on a genetic algorithm. The reaction rate parameters could be identified successfully by using a genetic algorithm and plant data. The effects of key operating variables (temperature, pressure, monomer feed ratio and residence time) on the reactor performance were also investigated. It was observed that the reactor performance strongly depends on the degree of dissolution of the solid terephthalic acid (TPA) in the reaction mixtures.

Modeling of Industrial High Pressure Autoclave Polyethylene Reactor Including Decomposition Phenomena

The aim of the present work is the development of a practical model for an industrial high-pressure polyethylene plant. The reactor considered in this work is the adiabatic slim type autoclave with four zones for free radical polymerization of ethylene. A fairly comprehensive but realistic model is described that has the ability to predict the temperature at each reaction zone as well as the effects of initiator flow changes. From the stability analysis we could identify the range of operating conditions which can effectively be used to prevent decomposition phenomena (runaway reactions) and to maximize polymer conversion in LDPE autoclaves.