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The performance of carbon molecular sieves and zeolite 5A was compared in a four-bed vacuum pressure swing adsorption process. The purpose of the process is to sequester CO2 from a CH4/CO2 mixture gas, such as coal bed methane or landfill gas. This study investigated the effects of the design variables and operating variables on methane purity, recovery, and specific power through simulations of the process using the two adsorbents. The adopted design variables for the investigation are the packing bed length and the diameter of the adsorption bed, and the selected operating variables are the adsorption pressure and vacuum pressure. The simulation results show that zeolite 5A is better than carbon molecular sieve in terms of power, especially under low-pressure operating conditions with a vacuum pressure of 1,000 Pa. However, carbon molecular sieves are better in terms of purity enhancement when the vacuum pressure is higher than approximately 2,000 Pa.

Energy source diversification through development of coalbed methane (CBM) resources is one of the key strategies to make a country less dependent on simple energy resources (e.g., crude oil, natural gas, nuclear energy etc.). Especially, enhanced coalbed methane (ECBM) technology can be expected to secure the resources as well as environmental benefits. However, the raw CBM gas obtained from CO2 ECBM contains a considerable amount of CO2, and the CO2 content increases depending on the operation time of the facility. Considering the changes of the CBM composition, we developed process simulations of the CBM separation & purification processes based on the amine absorption to meet the design specifications (CH4 purity of product stream: 99%, CH4 recovery rate: 99%) with different CBM feed gas conditions. Using the developed simulation model, we performed an economic evaluation using unit methane production cost (MPC) considering coal-swelling types and facility operation time, and established an operation strategy under different natural gas market scenarios.

This paper suggests an efficient optimization approach for the design of an integrated membrane bioreactor (MBR) system. Therefore, conceptual design studies to find optimal design conditions were performed with different constraints for the wastewater treatment using a mathematical modeling method. The integrated MBR system is composed of two anoxic tanks, two aerobic tanks, and one immersed membrane tank ( Janus, 2013 , Janus and Ulanicki, 2016 ). For the design of the system, the optimal process configurations, reactor volumes, and flow rates of each stream must be decided reasonably. Especially for the decision of the optimal process configurations, binary variables are generally employed as optimization decision variables for the mathematical modeling. Hence, a mixed integer nonlinear programming (MINLP) technique can be generally used to simultaneously determine the optimal design and operating conditions including process configurations. But if the MINLP problems are formulated as highly nonlinear dynamic equations, the optimal values of binary variables may sometimes be difficult to be properly decided. Thus, this paper introduces (1) the efficient optimization approach to solve the MINLP problems by parameterizing the binary variables leading to the reduction of the nonlinearity of the model, and (2) its applications to the conceptual designs of the integrated MBR system.

A dynamic simulation model of a hollow fiber membrane module has been developed by deriving new transient mole fraction balances, energy balances, and gas velocity equations based on mole balances. The adopted flow type of the membrane module is a counter-current flow for the recovery of methane from coalbed methane (CBM), which is a form of natural gas extracted from coalbeds and is composed of mainly methane (up to 80–99 vol%) and minor impurities (CO 2 , N 2 , etc.). This study assumes that the CBM feed gas for the dynamic simulation consists of 90% methane and 10% carbon dioxide. In the simulation model, CBM feed gas is supplied into the bore side feed, and purified methane rich gas, the retentate gas, is produced from the product side of the bores. Carbon dioxide rich gas, the permeate gas, is discharged from the outlet end of the shell side. The simulation results are very similar to the experimental data of Airrane Co., Ltd. in Korea. This paper presents the effects of design and operating conditions on the performance, and investigates the transient behaviors of membrane modules using the validated simulation model.

A novel optimization method, based on a mathematical modeling approach, for the design and operation of a hollow fiber membrane module is developed. The purpose of the membrane is to purify unconventional gases, such as coalbed methane gas, by removing carbon dioxide. For the design of the membrane module, the number of fibers within the module must be reasonably set as one of the design variables of the optimization problem. Therefore, a mixed integer nonlinear programming (MINLP) model including integer variables – i.e., the number of fibers in this study – is required to obtain optimal design conditions. However, if the integer variable is involved in the highly nonlinear model, the probability of success in the optimization calculation becomes very low because of the big computation load. Thus, this paper introduces: (1) a new optimization modeling technique which is able to solve the MINLP problem efficiently; (2) a successful scale up design optimization from laboratory (0.5 Nm3/h) to large commercial scale (500 Nm3/h); (3) design optimization results under different gas feed conditions, and (4) dynamic optimization results obtained by varying gas feed conditions.

The purpose of this conceptual design study is to evaluate coalbed methane (CBM) separation systems from an economic perspective. To achieve this goal, we propose several system configurations by combining membrane and pressure swing adsorption technologies with recycling. We then perform rigorous process simulations of the proposed CBM separation systems to meet the design specifications (CH 4 purity of product stream: 97%, CH 4 recovery rate: >90%) from a feed gas (CH 4 : 90% and CO 2 : 10%). We compare the systems using various technical and economic metrics, including methane recovery, process energy efficiency, and methane production cost. The top-performing system produces methane at a cost of 0.37 $/kg of CH 4 (6.9 $/MMBTU), which can be further improved up to 0.27 $/kg of CH 4 by applying promising scenarios.

This paper introduces an efficient scale-up design technology based on mathematical modeling and optimization approach. The target process is a vacuum pressure swing adsorption process which is preferred in purifying unconventional gas such as coalbed methane or landfill gas by sequestering carbon dioxide. The optimization results are validated very well from a pilot scale (10 N m3/h) to a large commercial scale (5000 N m3/h). The simulation and optimization model for the scale up design is established in gPROMS custom modeling tool. Judging from the reasonably very small differences between the optimization results and the vendor suggested data, it can be concluded that the scale-up design is very successful not by depending on the experimental and real plant experiences but by using a mathematical optimization modeling approach.

Coalbed methane is a worthwhile potential energy source, because methane gas is eco-friendly and a huge amount of coalbed methane has been buried in the United States, China, Australia, etc. This paper introduces a new simulation model of a vacuum pressure swing adsorption process that has been widely used to purify unconventional gases such as coalbed methane and landfill gas. The developed mathematical model includes a novel interstitial gas velocity equation derived by using a mole balance concept and an overall mass balance equation. The model is verified by comparing with three sets of laboratory-scale (about 0.5 Nm3h–1) experimental data and one set of pilot-scale (100 Nm3h–1) design data. Because the differences between the simulation results and the data are reasonably small, one can conclude that the 200 times scale-up design through the new simulation model is successful.

Natural gas liquefaction is a highly energy-intensive process due to its cryogenic operation. Therefore, a major concern in designing and optimizing natural gas liquefaction processes is the enhancement of energy efficiency. A liquefaction ratio in the natural gas liquefaction process affects the specific energy consumption, which is the energy consumption per unit mass of liquefied natural gas (LNG). However, most of the previous research has not considered the liquefaction ratio of the natural gas. This study focuses on minimizing specific energy consumption of a LNG plant considering the liquefaction ratio. To analyze the effects of the liquefaction ratio, four different cases are set and the optimizations are performed. The objective function to be minimized includes the total energy consumption and specific energy consumption. The result of minimizing total energy consumption converges on the lower bound of the liquefaction ratio, but the result of minimizing specific energy consumption converges bet...

This study investigates the effects of multistage compression on single mixed refrigerant processes in terms of specific work. Comparison of specific work published in the literature is not straightforward due to the variety of compression configurations and the design bases. Therefore, four configurations (two-, three-, and four-stage and pump-added three-stage compressions) along with three natural gas compositions were considered. To compare with the simulation and optimization results in the literature, these 12 cases, having the same design basis, were optimized by adjusting the optimization variables such as the flow rate and composition of the refrigerant, the compression ratio of each compressor, the inlet pressure of the first compressor, and the outlet temperatures of the hot and cold refrigerant streams. There were two important findings: (1) adding a pump reduces specific work more than adding a compressor or decreasing the minimum temperature difference value in the compressors; (2) among the...

Natural gas liquefaction is an energy-intensive process in which energy reduction is a main concern. This research focused on minimizing the energy of the pure refrigeration cycle in natural gas liquefaction by improving the subcooling system. To minimize energy consumption, a pure refrigeration cycle with a subcooling system was simulated, and the result was thermodynamically analyzed. The thermodynamic analysis identified an opportunity to reduce the energy consumption, and a new design was proposed for the subcooling system. In addition, the proposed design was deterministically optimized to find the optimal compressing ratio, temperature, pressure, and flow rate. As the result, the optimal operating conditions were determined, and the energy consumption was reduced by 17.74%.

One of the most important challenges in a natural gas liquefaction plants is to improve the plant energy efficiency. In particular, if part of the natural gas is used as a fuel gas or the liquefaction ratio is taken into account as a design factor in an liquified natural gas (LNG) plant, process design focusing on cold energy recovery is an attractive option. In this study, various energy recovery-oriented process configurations and the potential improvements of energy savings in LNG plants were analyzed. Our primary focus for energy recovery in the LNG liquefaction process was centered on utilizing the flash gas stream from the phase separator. The applicability of the proposed configurations was validated by modeling and simulation of the single mixed refrigerant (SMR), propane precooled mixed refrigerant (C3MR), and single nitrogen (N2) expander processes. The simulation results for all cases exhibited considerable reductions of refrigerant flow rates, seawater cooling duties, and the specific work. Fo...

A non-isothermal dynamic optimization model of direct methanol fuel cells (DMFCs) is developed to predict their performance with an effective optimum-operating strategy. After investigating the sensitivities of the transient behaviour (the outlet temperature, crossovers of methanol and water, and cell voltage) to operating conditions (the inlet flow rates into anode and cathode compartments, and feed concentration) through dynamic simulations, we find that anode feed concentration has a significantly larger impact on methanol crossover, temperature, and cell voltage than the anode and cathode flow rates. Also, optimum transient conditions to satisfy the desired fuel efficiency are obtained by dynamic optimization. In the developed model, the significant influence of temperature on DMFC behaviour is described in detail with successful estimation of its model parameters.

This work focuses on the optimization of cyclic adsorption processes to improve the performance of CO2 capture from flue gas, consisting of nitrogen and carbon dioxide. The adopted processes are the PSA (pressure swing adsorption) process and the FVPSA (fractionated vacuum pressure swing adsorption) process, modified from the FVSA (fractionated vacuum swing adsorption) process developed by Air Products and Chemicals, Inc. The system models are currently bench scale and adopt zeolite13X as an adsorbent. The high-temperature PSA is better for high purity of product (CO2), and the high-temperature FVPSA is much better than the normal-temperature PSA processes. The main goal of this study is to improve the purity and recovery of carbon dioxide. The Langmuir isotherm parameters were calculated from experimental data taken at National Energy Technology Laboratory (Siriwardane, R.; NETL, DOE, 2004). Moreover, efficient optimization strategies are essential to compare these processes. To perform optimization work...

Recent developments in the fuel cell industry have yielded an increase in expected demand for a pure hydrogen source; one attractive method for meeting this demand involves separating hydrogen from a hydrogen-methane mixture using pressure swing adsorption (PSA). In designing PSA systems, the selection of the adsorbent and operational flowsheet are the most important and limiting decisions; beyond this, implementing systematic methods to find optimal operating conditions and bed parameters is essential. These decision variables include flowrates, bed pressures, step times and bed dimensions. This research examines this PSA system via dynamic simulation and optimization; the primary purpose of the optimization is to maximize the recovery of the key components while meeting both the hydrogen purity and cyclic steady state requirements. In this study, the process model is formulated for this separation using gPROMS. The model discretizes the spatial domain, and binary variables are used to adjust the boundary conditions for the bed as the system runs through the four-step PSA cycle. Two alternative PSA cycles are simulated, optimized and compared to assess the best performance for this PSA separation.

A pressure-swing adsorption process, which uses zeolite 13X as an adsorbent to recover and sequester carbon dioxide from mixture gas (nitrogen and carbon dioxide), is investigated through dynamic simulation and optimization. The purpose of this paper is to improve the purity of each component by finding optimal values of decision variables with a given power constraint. Langmuir isotherm parameters are calculated from experimental data of zeolite 13X and a general mathematical model consisting of a set of partial differential and algebraic equations and solved in gPROMS. The method of centered finite differences is adopted for the discretization of the spatial domains, and a reduced space SQP method is used for the optimization. As a result, the optimal conditions at cyclic steady state are obtained.

In this study were performed dynamic simulations of a cyclic thermal swing adsorption (TSA) process to analyze the effects of contact time on the dynamic behaviors and were compared the breakthrough curves of simulations with those of experiments. The fixed bed of TSA is charged with activated carbon as an adsorbent to purify and regenerate the ternary mixtures consisting of benzene, toluene, and p-xylene. The conditions are nonequilibrium, nonisothermal, and nonadiabatic. The cyclic steady-state (CSS) cycles are obtained under various conditions by dynamic simulations. That is to say, this work perceived the influences of purge gas velocity and bed length on breakthrough curves, CSS convergence time, cyclic operating step time, purge gas consumed, regeneration energy requirement, and adsorption ability at CSS.

This paper focuses on the development of a multiobjective optimization approach and the first application to highly nonlinear cyclic adsorption processes such as thermal swing adsorption (TSA) and rapid pressure swing adsorption (RPSA). The new method was modified from the conventional summation of a weighted objective function (SWOF) method, which is one of the most traditional and the simplest way of multiobjective optimization programming. The new modified SWOF technique eliminates the main drawbacks of the conventional SWOF and approximates the Pareto curve efficiently with several Pareto points as explained by the two examples (TSA and RPSA). The sensitivity of these processes was also analyzed.

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 Yun's 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.

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.

This paper proposes a novel design of a thermal swing adsorption (TSA) system with a cooling jacket and compares it with conventional TSA processes by cyclic simulations. The purposes are to increase the adsorption efficiency and to find a suitable condition for its application to a two-bed TSA system for continuous operation. These processes purify the ternary mixtures consisting of benzene, toluene, and p-xylene. The models are based on nonequilibrium, nonisothermal, and nonadiabatic conditions. The breakthrough curves of our simulation model are compared with that of Yun's experiments (Ph.D. Thesis, Korea University, 1999). As a result of the continuous simulation from startup to the cyclic steady state (CSS), the augmented ratio of the amount adsorbed of jacketed TSA system to the conventional one at CSS is 20.3% for a two-step operation and 6.3% for a three-step operation, respectively.

This study analyzes the effects of the purge gas temperature on the dynamic behaviors of a cyclic thermal swing adsorption (TSA) process by dynamic simulations. This TSA process adsorbs and regenerates the ternary mixtures consisted of benzene, toluene and p-xylene (BTX). The models are based on nonequilibrium, nonisothermal and nonadiabatic conditions. The breakthrough curves of our simulation model are compared with those of Yun's experiments (1999). The cyclic steady state (CSS) cycles are calculated for the various cases. The influences of purge gas temperature on breakthrough curves, purge gas consumed, regeneration energy requirement at CSS are also discussed for optimal operation.

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.

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.

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.

We have developed a symbolic verification method for determining the safety and operability of chemical process sequential control systems. The number of test cases required to verify a system grows exponentially as the number of components of the system increases. This state explosion problem limits our previous automatic verification method (Moon et al., 1992, Moon, 1994) to testing small systems. To mitigate this problem, we have adopted the Symbolic Model Verifier (SMV) which was originally developed by McMillan to test VLSI circuits. The method uses Boolean formulas to represent sets and relations in order to avoid building an explicit state transition graph which occupies most of the computer memory consumed for the computation. Ordered Binary Decision Diagrams are employed to manipulate the formulas efficiently in the model checking process. As a result, the SMV can verify large alarm systems including 10121 reachable states. The input language of SMV also makes the modeling of chemical processing systems as easy and less error prone processes. The method is demonstrated and the performance of the verifier is studied in a series of multiple alarm designs.

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.

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.

Publisher Summary This chapter presents a new design of a thermal swing adsorption (TSA) process-cooling jacket installed under the condition of operating the continuous two-bed TSA process. TSA operation consists of two steps––adsorption and regeneration––or three steps––adsorption, regeneration, and cooling. The jacketed TSA bed enhances the adsorption efficiency and saves the consuming rates of purge gas consumption and regeneration energy compared with the previous TSA bed. The target species are benzene and toluene. As a result of dynamic simulations from start-up to cyclic steady state (CSS), the adsorption ratios are 96% for benzene and 100% for toluene in the jacketed TSA bed while the adsorption ratios are 82% for benzene and 98% for toluene in the previous two-step operating TSA bed. The jacketed TSA bed saves the consuming rate of the purge gas by 53.47 % and that of the regeneration energy by 52.63 %, compared with the previous three-step operating bed without jacket.

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.

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.