Daeho Ko

Analysis of purge gas temperature in cyclic TSA process

This study analyzes the effect of an operating parameter on the dynamic behavior by performing dynamic simulations of cyclic thermal swing adsorption (TSA) system, in fixed beds packed with activated carbon as an adsorbent. This TSA process purifies and regenerates the ternary mixtures consisted of benzene, toluene and p-xylene. A mathematical model, considering the dynamic variation and spatial distribution of properties within the bed, has been formulated and described by a set of partial differential algebraic equations. The models are based on non-equilibrium, non-isothermal and non-adiabatic conditions. The breakthrough curves of our simulation model are compared with those of Yuns experiments (1999). The cyclic steady-state (CSS) cycles are obtained for the various cases by cyclic simulation. The influences of the purge gas temperature on breakthrough curves, CSS convergence time, cyclic operating step time, purge gas consumed, regeneration energy requirement and adsorption ability at CSS are also discussed.

Optimization of mixed-refrigerant system in LNG liquefaction process

Abstract LNG liquefaction is an energy intensive process. For this reason, various liquefaction processes for saving energy exist. Searching optimal process condition is very difficult because of its high nonlinearity. Pressure level, refrigerant flowrate, and refrigerant composition are key operation variables to reduce energy consumption. These variables play an important role in affecting the overall performance. Compressor is a major energy-consumption unit in LNG plant and it spends much energy depending on operating conditions. In this paper, searching optimal condition is carried out by using simultaneous optimizations. NLP model is applied for the SMR process. The optimization result shows that refrigerant composition is a major key variable and half of energy consumption can be reduced by changing operating conditions and refrigerant composition only.

Optimization of start-up operating condition in RPSA

Abstract This study focuses on an optimization of start-up operating conditions of a rapid pressure swing adsorption (RPSA) process, which is operated in a cyclic pressure variation mode. The objective function is defined not only to reduce the operating power but also to shorten the time to reach the cyclic steady state (CSS), as well as to increase the purity of the desired product at CSS. A general mathematical model considering the dynamic variation and spatial distribution of properties within the bed has been formulated and described by a set of integrated partial differential algebraic equations (IPDAE). The number of variables for optimization is 16 825 and both the single discretization of a spatial domain and the double discretization of spatial/time domains have been used for the numerical integration. As the computation result the optimal cycle time is 14.46 s and the optimal feed pressure is 597 kPa. Under the optimal condition the purity of desired product at CSS is calculated as 96.42% and the CSS convergence time is 5857 s.

Development of a rescheduling system for the optimal operation of pipeless plants

Abstract This paper focuses on the development of a rescheduling system to cope with unexpected process events in pipeless plants. Since the pipeless batch plant relies on mobile vessels for the transfer of material from one stage of processing to another, the process variations may be more often than any other batch processes. The process deviations from original schedule are mainly process time variations. This rescheduling system for the pipeless batch plant overcomes the processing time variations by adjusting the starting time of reactions and determining the sequence of equipment to be processed. So it treats the earliness of operations as well as tardiness of reactions efficiently. In this study, the rescheduling system is implemented and two main approaches. for the solution of various cases are proposed.

Rescheduling algorithms in case of unit failure for batch process management

Abstract Adjusting the operating schedule in real time is very important in constructing CIM and improving the safety and productivity of chemical processing systems. Computation for generating an optimal schedule at the scheduling level mostly requires a long time, so it is difficult to respond immediately to actual process disturbances such as unit failures, the absence of operators and reaction time variations. To modify the original schedule in real time, we developed rescheduling algorithms such as DSMM(Dynamic Shift Modification Method), PUOM(Parallel Unit Operation Method) and UVVM(Unit Validity Verification Method). Their main functions are to minimize the effects of unexpected events and to modify the schedule desirably corresponding to process time variations. As a result, the algorithms generate a new pertinent schedule in real-time which is close to the original schedule in order to provide an efficient way of responding to the variation of process environment, minimizing the inventory cost. Examples of shampoo production batch processes illustrate the efficiency of the algorithms.

Development of a Batch Manager for Dynamic Scheduling and Process Management in Multiproduct Batch Processes

A batch manager is developed for the dynamic scheduling and on-line management of process operations. The developed system consists of a process monitoring module and a dynamic scheduling module. When a deviation from the initial schedule is detected in a process monitoring module, dynamic scheduling is performed in the dynamic scheduling module and the initial schedule is adjusted to the proper schedule by using rescheduling algorithms presented in this paper. The adjusted schedule is shown in the process monitoring module. The dynamic scheduler in the batch manager copes with several unexpected process events of batch process operations by adjusting the EST (Earliest Start Time) of equipment, redetermining the batch path and reassigning tasks to equipment. This study focuses on the implementation of a batch manager with on-line dynamic scheduling for batch process management. Examples of fodder production batch processes illustrate the efficiency of the algorithms.

Multiobjective optimisation for environment-related decision making in paper mill processes

Considering only environmental impact or only profit is not enough to manage chemical processes. This study focuses on the optimal scheduling of cutting papers using the multiobjective optimisation programming. Two independent objective functions are introduced in this paper, the production cost and trim loss with the constraints of satisfying the customer orders. The Goal Constrained Programming (GCP) algorithm is created for the multiobjective optimisation with priority. The proposed algorithm takes the deviation for the weighting factor. The GCP provides various optimal schedule sets satisfying the economic and environmental requirements.

Dynamic optimisation of CH4/CO2 separating operation using pressure swing adsorption process with feed composition varies

Pressure Swing Adsorption (PSA) is widely used process for gas separation. Recently, some researchers have been trying to use this PSA process for upgrading bio-gas. The bio-gas mainly consists of methane and carbon dioxide. Highly purified methane gas can be used for energy production, when the methane gas is separated from the carbon dioxide. However, during bio-gas extraction the composition and flow rate are slowly changing. Due to these changes, undesirable product gas properties of will be obtained. The efficiency of PSA process including recovery, purity and productivity are affected by operating conditions, such as feed pressure, feed velocity, p/f ratio, step-time etc. The aim of this research is dynamic optimization of PSA operation for bio-gas upgrading process considering feed composition variations. The objective is maximization of methane recovery, at the purity constraints, while control variables are step times for each step and Purge/Feed (P/F) ratio at regeneration step. In this research, robust PSA model is developed for dynamic simulation and optimization using gPROMSTM to solve problem. For improving accuracy of the model, distribution method is used; Central Finite Difference Method (CFDM), 2 level in this model. Especially, the time variables are treated as control variables in this model. Due to the discrete changes of boundary and equations, the solving of this optimization problem needs high skills and strategies. The ‘SRQPD’ solver, one of the NLP solvers, has been used, applying new equations with binary variables which can describe which time belongs to which step.

Current status of optimal design of natural gas liquefaction process

Abstract Natural gas liquefaction process is an energy intensive process due to its cryogenic condition. Therefore, one of the major objectives for the design and the optimization is the minimizing total energy consumption. Another important objective is the minimizing cost of the energy supply system. This research focused on the cost based optimization. First, the energy minimization for the mixed and the pure refrigerant systems was performed by deterministic optimization model. Then, the cost minimization for energy supply to the process was performed by the driver selection model. The driver selection model was built as mixed integer linear programming (MILP). As the result, 15 to 17% of energy saving was checked for the refrigerant system. At that time, optimal driver set which is the minimum cost for the energy supply system was found.

Automatic accident scenario generation and multiobjective optimization for safety-related decision making in chemical processes

Abstract Safety investment in Chemical Process Industries (CPI) has been required regarding process safety together with economic aspects. This paper concerns an automatic accident scenario generation and multiobjective optimization method for finding the most effective investment scenario set in CPI. Accident scenarios make up a decision pool for safety investment, and the multiobjective optimization method determines the efficient investment scenario set under the given constraints, such as a limited budget, environmental requirements and social demands.