• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.12
• /
• pp.1163-1168
• /
• 2006

Manufacturing technology for the production of high value-added fine chemical products is emphasized and getting more attention as the diversified interests of customers and the demand of high quality products are getting bigger and bigger everyday. Thus, the development of advanced batch processes, which is the preferred and most appropriate way of producing these types of products, and the related technologies are becoming more important. Therefore, high-precision batch distillation is one of the important elements in the successful manufacturing of fine chemicals, and the importance of the process operation strategy with quality assurance cannot be overemphasized. Accordingly, proposing a process structure explanation and operation strategy of such processes including batch processes and batch distillation would be of great value. We investigate optimal operation strategy and production planning of multi-purpose plants consisting of batch processes and batch distillation for the manufacturing of fine chemical products. For the short-term scheduling of a sequential multi-purpose batch plant consisting of batch distillation under MPC and UIS policy, we proposed a MILP model based on a priori time slot allocation. Also, we consider that the waste product of being produced on batch distillation is recycled to the batch distillation unit for the saving of raw materials. The developed methodology will be especially useful for the design and optimal operations of multi-purpose and multiproduct plants that is suitable for fine chemical production.

• Korean Chemical Engineering Research
• /
• v.50 no.2
• /
• pp.270-276
• /
• 2012

In this study, thermally coupled distillation system and conventional two-column process were investigated for extractive distillation. The two processes were simulated and optimized using Aspen plus. Objective function for the optimization was energy consumption and optimization results to reduce energy consumption were used to get guidelines for design and operation for the two extractive distillation processes. Comparison of these two processes showed that thermally coupled distillation system provided better energy efficiency and lower capital cost than conventional distillation system.

• Korean Chemical Engineering Research
• /
• v.46 no.5
• /
• pp.892-897
• /
• 2008

A new ethanol dehydration process utilizing a thermally coupled distillation column is proposed to reduce the energy requirement of the existing dehydration processes. An entrainer of benzene is used in the proposed system having the column profile similar to the equilibrium composition profile for the maximum distillation column efficiency, and the feed composition is arranged to close to the boundary of different distillation regions. It is found that the proposed distillation system gives some 18% of energy saving over the existing process. In addition, design guidelines are suggested for other azeotropic distillation process.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.742-748
• /
• 1993

The dynamics and control of two complex column configurations (sidestream column with stripper; prcfractionater/sidestream column configuration), which are multivariable interacting and nonlinear, have been studied. A new control scheme developed by Hanand Park(1993) to deal with the nonlinear and multivariable nature of distillation processes has been applied to these complex distillation configurations. The control scheme incorporates a nonlinear wave model into a generic model control framework. An observer based on the nonlinear wave model is used to determine the profile positions of distillation column sections. The control scheme enables tight control of the profile position of each column section that leads to fast stabilization of product compositions.

• Applied Chemistry for Engineering
• /
• v.31 no.5
• /
• pp.520-525
• /
• 2020

In this study, we developed a machine learning-based model for predicting the production stage temperature of distillation process. It is necessary to predict an accurate temperature for control because the control of the distillation process is done through the production stage temperature. The temperature in distillation process has a nonlinear complex relationship with other variables and time series data, so we used the recurrent neural network algorithms to predict temperature. In the model development process, by adjusting three recurrent neural network based algorithms, and batch size, we selected the most appropriate model for predicting the production stage temperature. LSTM₁₂₈ was selected as the most appropriate model for predicting the production stage temperature. The prediction performance of selected model for the actual temperature is RMSE of 0.0791 and R² of 0.924.

• Membrane and Water Treatment
• /
• v.10 no.5
• /
• pp.387-394
• /
• 2019

Membrane distillation (MD) is one of the water treatment processes which involves the momentum, heat and mass transfer through channels and membrane. In this study, CFD modeling has been used to simulate the heat and mass transfer in the direct contact membrane distillation (DCMD). Also, the effect of operating parameters on the water flux is investigated. The result shows a good agreement with the experimental result. Results indicated that, while feed temperature is increasing in the feed side, water flux improves in the permeate side. Since higher velocity leads to the higher mixing and turbulence in the feed channel, water flux rises due to this increase in the feed velocity. Moreover, results revealed that temperature polarization coefficient is rising as flow rate (velocity) increases and it is decreasing while the feed temperature increases. Lastly, the thermal efficiency of direct contact membrane distillation is defined, and results confirm that thermal efficiency improves while feed temperature increases. Also, flow rate increment results in enhancement of thermal efficiency.

• Plant Journal
• /
• v.13 no.3
• /
• pp.26-29
• /
• 2017

In this research, the composition of the feed stream is optimized in pressure swing distillation for separating of methanol-acetone. It is well known that the composition of feed stream in pressure swing distillation system has a great influence on the feasibility to separate mixture. The workscope of this study is to show better separation efficiency at specified pressure by controlling the composition of feed stream with recycle of two products. Based on the base case without recycle flow, two processes are designed that methanol and acetone are recycled to feed stream, respectively. Each processes are compared with total annual cost and as a result, the base case without recycle flow are most favorable.

• Korean Chemical Engineering Research
• /
• v.58 no.4
• /
• pp.565-572
• /
• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.1109-1114
• /
• 1992

Modern chemical processes are becoming more complicated. The sophisticated chemical processes have needed the fault diagnosis pxpert systems that can detect and diagnose the fault diagnosis expert systems that can detect and diagnose the faults of some processes and give and advice to the operator in the event of process faults. We present the Time-Delay Neural Network(TDNN) approach for on-line fautl diagnosis. The on-line fault diagnosis system finds the exact origin of the fault of which the symptom is propagated continuously with time. The proposed method has been applied to a pilot distillation column to show the merits and applicability of the TDNN.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1826-1829
• /
• 2003

Fine chemical production must assure high-standard product quality as well as characterized as multi-product production in small volumes. Installing high-precision batch distillation is one of the common elements in the successful manufacturing of fine chemicals, and the importance of the process operation strategy with quality assurance cannot be overemphasized. In this study, we investigate the optimal operation strategy and production planning of a sequential multi-purpose plants consisting of batch processes and batch distillation with unlimited intermediate storage. We formulated this problem as an MILP model. A mixed-integer linear programming model is developed based on the time slot, which is used to determine the production sequence and the production path of each batch. Illustrative examples show the effectiveness of the approach.