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Roll-to-roll (R2R) printing systems provide a continuous process of thin, flexible material (called web) through printing cylinders, where the required patterns are printed on the web. Tension fluctuation caused by the upstream register control quantity may lead to the downstream register errors. Thus, it is a challenge to develop a register control algorithm to remove the couplings among the angular velocity, tension fluctuation, and register error. In this article, the register error is defined as the marks position misalignment between the first and the current printing unit. Based on the definition, a mathematical model of registration is derived. Based on the model, a fully decoupled proportional-derivative (FDPD) control algorithm is proposed to completely remove the coupling between the upstream register control quantity and the downstream register error. Simulations and experiments are carried out to demonstrate the effectiveness of the proposed control algorithm. Compared with the other compensation control methods, FDPD is a complete decoupling control method, which can control the register errors of all downstream printing units (more than seven printing units) within $\pm 0.05$ mm, even if the upstream printing unit has a 3 mm register error. Decoupling formula included in the FDPD control is a first-order compensation formula, which is easy to implement in the actual industry. Furthermore, the proposed FDPD can deal with the situation where two adjacent gravure cylinders have different web lengths, which is not taken into account by other control methods.

Post-combustion carbon capture (PCC) using a chemical solvent is the most mature and well-proven technology to mitigate carbon dioxide (CO2) emission. However, substantial energy consumption for solvent regeneration is still a critical challenge for widespread deployment of this PCC. In this study, a novel low-energy, combined rich and lean vapor recompression (RLVR) process for the CO2 capture process using dilute aqueous ammonia (NH3) solvent was developed and optimized by integrating of rich vapor recompression (RVR) and lean vapor recompression (LVR) approaches. A parametric study showed that the minimum total equivalent work can be obtained when CO2 loading and NH3 concentration of lean solution are 0.275 mol CO2/mol NH3 and 5.0 wt%, respectively and the pressures of stripper, rich and lean solvent flash drums are maintained at 10.5 bar, 4.62 bar and 6.93 bar, respectively. Under such operation conditions, the total equivalent work required for a CO2 capture plant was remarkably reduced to 0.123 kW h/kg of CO2 captured, which is equivalent to about 10% energy penalty. This amounts to 26–54% reduction from recent literature reports. Furthermore, this combined RLVR process can completely eliminate the need of reboiler, leads to improving the flexibility of the capture plant since it can be separated from power plant steam systems.

Remaining useful life (RUL) prediction of bearings is important to guarantee their reliability and formulate a maintenance strategy. Recently, deep graph neural networks (GNNs) have been applied to predict the RUL of bearings. However, they usuallylack dynamic features, use manual stage identification, and experience the over-smoothing problem, which will have a negative effect on the prediction accuracy. This paper proposes a new framework for bearing RUL prediction based on spatial-temporal multi-scale graph convolutional neural network (STMSGCN), which can improve the accuracy of prediction by solving the above-mentioned problems. Specifically, different to the most-used static feature of bearings, a dynamic feature that can capture the time-varying change of vibration energy is proposed. A sliding window alarm method is proposed to detect the fault occurrence time (FOT), which can offer an accurate healthy stage rather than human-defined methods. Then, the STMSGCN is proposed to predict the RUL of bearings, which solves the over-smoothing problem of the deep GNN model. The PRONOSTIA platform is adopted to verify the proposed method. The results verify that the sliding window alarm method can detect the FOT faster for slowly degrading bearings, and the proposed STMSGCN structure gives higher prediction accuracy compared to the existing methods.

Large-scale solid oxide fuel cell (SOFC) systems create operational challenges owing to their size and scale. An SOFC dynamic digital twin model can help users understand the system status and determine the operating action. In this study, a dynamic digital twin of a 25 kW SOFC plant was presented as a simulator to help operators safely and stably determine the operating conditions for a real commercial SOFC plant. In the scale-up process, the data from the 1 kW SOFC pilot plant were used to analyze the reliability and electrode parameters and develop a digital twin model that matches actual conditions at the plant. The scale-up digital twin model is applied to a 25 kW SOFC system, in which the SOFC is connected with upstream reformers, downstream burners, and multiple heat exchangers. The 25 kW SOFC digital twin model was validated by steady-state data and applied to on-site operation prediction with very high accuracy. The digital twin was used as an operating reference in the field, and the results showed that it could effectively help operators determine operation strategies using simulations to safely and stably execute the process.

Many continuous industrial processes operate in different steady states with different grades or products. The switching between two steady states is called transition. Transition consists of a series of operation changes that should be carried out in proper order, within certain magnitudes and time region. Since faulty operation may lead to increase in inferior products or even hazard events, monitoring of the transition is desired. In this work, a transition identification and monitoring scheme is proposed based on slow feature analysis. Two monitoring statistics which represent the location of the trajectory and the speed of transition are proposed. Besides, operating faults are generated based on the guidewords of hazard and operability analysis (HAZOP). Using a numerical case and the mode 4-to-2 transition of the Tennessee-Eastman process in which catastrophic failures exist, the effectiveness of the proposed method is validated. In addition to missed detection rate and false alarm rate, two performance indexes known as detection time (DT) and rescue time (RT) are introduced. The advantages of proposed method are benchmarked against the stage-based sub principle component analysis(sub-PCA) and the global preserving statistics slow feature analysis(GSSFA).

In industrial process, fault isolation technology will identify the major variables leading to faults. The existing fault isolation methods usually have ”smearing effect”, cannot identify the sensitivity of the fault variables, or require too much calculation. To solve the above problems, a multivariate fault isolation method based on between-class difference analysis and multidimensional reconstruction-based contribution (RBC) is proposed in this work. First, two kind of strategies, i.e.,principle component analysis (PCA) and fisher discriminant analysis (FDA), are adopted to obtain the sensitivity indicators of the variables to the faults respectively, which is taken as the basis for determining the optimal reconstruction direction. Then, multidimensional RBC is used to determine the number of fault variables based on the selected reconstruction direction. Finally, the primary and secondary fault variables are isolated according to their fault sensitivities. The feasibility of the proposed method is illustrated by a numerical simulation example and TE process. To sum up, the proposed method provide sensitivity information of each fault variables and high diagnosis rate. Moreover, it also has an advantage in reducing the calculation time because of the preliminary selection of fault variables.

In an ammonia-based post-combustion carbon capture (PCC) process, the regeneration energy of CO2-lean solvent comprises the main fraction of overall energy consumption. The use of a pressurized CO2 stripper to enhance the CO2 purity in the overhead vapor is one option for reducing the regeneration energy. Further energy reductions can be achieved by modifying stripper configurations reported in the literature. In this study, the energy-saving performance of advanced stripper configurations is investigated, including cold split bypass (CSB), lean vapor compression (LVC), and rich vapor compression (RVC). Our results show that the energy reduction by CSB has been underestimated in the literature because the effect of the warm rich feed stage has been neglected. Compared with a standard stripper operated at 10 atm, the CSB modification achieves 34.2% of energy reduction, and the combination of CSB and LVC reaches 34.4%. In other words, LVC only improves the energy saving of CSB by 0.2%.

In this study, a computational fluid dynamics (CFD) model of a sieve plate scrubber was built to predict its particle-removal efficiency and predict the U-shaped curve of the particle-removal efficiency as particles became smaller. Due to the complexity of particle tracking, it takes considerable time to simulate the model by using a three-dimensional (3D) structure, which is not conducive to finding the appropriate setting of particle forces. Instead, we presented a dimension-reduction method to estimate the particle force setting by using a two-dimensional (2D) structure. The rationality of the dimension-reduction method was validated by the consistency in froth density for both 2D and 3D models at various air-inlet velocities. Furthermore, the result of the particle forces setting showed that besides the drag force, other forces, such as the lift force, pressure-gradient force, gravity force, and virtual mass force, should be employed in the CFD model to predict the particle-removal efficiency of the sieve plate scrubber. The prediction results of the 2D model remarkably matches the particle-removal efficiency results of experimental data from the literature for various gas velocities and particle sizes. In addition, the model predicts the U-shaped curve of the particle-removal efficiency for the particle-diameter range from 0.1 to 1.0 μm. Furthermore, a 3D model with the setting of the particle forces as in the 2D model was used to validate the consistency between the 2D and 3D models. The result showed that the particle-removal efficiency of the 3D model was considerably close to the prediction results of the 2D model.

A real-time quantitative analytical approach, differential mobility analysis coupled to gas-phase Fourier-transform infrared spectroscopy (DMA/FTIR), is demonstrated for an aerosol spray-based synt...

Soft sensors attempt to predict the key quality variables that are infrequently available using the sensor and manipulated variables that are readily available. Since only limited amount of labeled data are available, there is always the concern whether the underlying physics were captured so that the model can be reasonably extrapolated. A sequence-to-sequence model in the form of a nonlinear state-observer/encoder and predictor/decoder was proposed. The observer can be trained using a large amount of unlabeled data, but in a supervised manner in which the process dynamics is tracked. The encoder output and manipulated variables are used to train the quality predictor. The model is applied to the product impurity predictions of an industrial column. Results show that good predictions and excellent consistency in the sign of estimated gains can be achieved even with limited amount of data. These findings indicated that the proposed sequence-to-sequence data-driven approach is able to capture the underlying physics of the process.

Computational fluid dynamics (CFD) simulations of countercurrent gas–liquid flow in random packings of Raschig rings were carried out in this study. This model used gravity simulation to construct the random packing structure, and a volume expansion-recovery method to improve the meshing quality. A simple feedback control scheme was applied to control the gas inlet flow rate so that pressure drop can be estimated. The generated characteristics of the packing structure such as the number of packing elements, dry surface area and porosity were found to be close to experiments. CFD predictions of hydrodynamics properties are also validated by the experimental data using a small column section. The results indicate that our CFD model was able to capture the essential hydrodynamics behavior of the gas–liquid countercurrent flow. Hence, the approach presented in this study can be used as a basis for studying the effect of detailed packing-geometry design on hydrodynamic and mass transfer characteristics.

Shelf-life prediction of liquid-type (such as nano-sol) products is an interesting research topic. Recently, a pH acceleration method has been proposed in the literature to address the shelf-life p...

In this paper, an extended state observer (ESO)-based run to run (RtR) controller is presented to suppress various stochastic disturbances emerging in the semiconductor manufacturing process. Using the internal model control theory, the relationship between the ESO-RtR controller and the exponentially weighted moving average (EWMA) controller, and the relationship between the ESO-RtR controller and double EWMA controller are discussed. The stable region of the proposed ESO-RtR controller is calculated and discussed by using the Jury’s test. Furthermore, the ESO-RtR controller is extended to a nonlinear form (i.e., nonlinear ESO-RtR controller) to improve its disturbance compensation capability. Finally, numerical simulations and an industrial example are provided to demonstrate the effectiveness of the proposed algorithm.

Hexamethylene-1,6-diisocyanate (HDI) is one of the most significant aliphatic diisocyanates for polyurethane production. The thermal decomposition of dimethylhexane-1,6-dicarbamate (HDC) has been considered as a very attractive non-phosgene route for HDI synthesis to realize industrialization because HDC can be synthesized by reacting 1,6-hexanediamine (HDA) with a green component, dimethyl carbonate (DMC). In this study, the optimal separation process design for non-phosgene HDC synthesis is investigated. Three design strategies to separate reaction products are proposed here. In the first two strategies, the low-boiling mixture containing un-reacted DMC and byproduct methanol is almost completely withdrawn from a column top, while some amount of un-reacted DMC is deliberately designed to be drawn out from the column bottom in the third strategy. Optimal processes designed by these three strategies are further enhanced by heat integration to reduce energy consumption. Simulation results reveal that the processes designed by strategy 3 need the least total annual cost and much economic benefit is achieved by the technologies of internal and external heat integrations. The key cost saving from internal heat integration is attributed to fully take advantage of the characteristic of DMC–methanol azeotrope at high pressure, and the higher column top temperature rendering condenser duty totally utilized by column bottom.

In the development of soft sensors for industrial processes, the availability of data for data-driven modeling is usually limited, which led to overfitting and lack of interpretability when conventional deep learning models were used. In this study, the proposed soft sensor development methodology combining first-principle simulations and transfer learning was used to address these problems. Source-domain models were obtained using a large amount of data generated by dynamic simulations. They were then fine-tuned by a limited amount of real plant data to improve their prediction accuracies on the target domain and guaranteed the models with correct domain knowledge. An industrial C4 separation column operating at a refining unit was used as an example to illustrate the effectiveness of this approach. Results showed that fine-tuned networks could obtain better accuracy and improved interpretability compared to a simple feedforward network with or without regularization, especially when the amount of actual data available was small. For some secondary effects, such as interaction gain, its interpretability is mainly based on the interpretability of the corresponding source models.

Data-driven operation monitoring and optimization of chemical plants can be performed using Digital Twin, along with intelligent algorithms. This study proposed a sequence-to-sequence rolling training algorithm to overcome the challenge of rolling predictions. The data were generated through dynamic simulation of the vapor-recompression C3 process using Aspen Plus. Studies showed that StS with rolling training could better fit the real data than the StS model. Moreover, StS with rolling training was able to present efficient long-term predictions as Digital Twin.

Background We present a dynamic model for designing a rotating packed bed (RPB) demonstrating volatile organic compound absorptions with circulation. The developed RPB model can be applied for industrial process simulations and scale-up. Methods The industrial applicability of the model was validated using in-house steady-state and dynamic RPB experimental data. The absorption of acetic acid (AA) in gas stream by an RPB, evaluated using reverse osmosis (RO) water and 1 wt.% NaOH aqueous solution, established the model. The operational variables’ effects, such as rotational speed and gas to liquid ratio, on the AA absorption efficiency are discussed. Significant findings The height of transfer unit (HTU) of the RPB was less than 3 cm, indicating the superior mass-transfer efficiency of the RPB than that of conventional packed bed column. The RPB model could predict the HTU in a steady-state well for the AA absorption, and the average absolute deviations (AAD) of HTU for the absorbent using RO water and 1 wt.% NaOH solution were 19.2% and 15.2%, respectively. Furthermore, the RPB model accurately predicted the AA absorption in circulation well for 1 wt.% NaOH with initial AA loadings of 1.7, 1.9, and 2.0 wt.% and AADs of 3.5%, 1.7%, and 1.9%, respectively.

A monoethanolamine (MEA) aerosol growth model was developed to quantify the aerosol growth factor in an amine-based CO2 capture absorber that considers the gas-liquid interactions, and it is empirically validated by measuring the aerosol particle size and concentration. The aerosol growth model, using sucrose as the aerosol nuclei instead of sulfuric acid to prevent the corrosion of the test equipment, accurately predicted that the outlet aerosol size increased to the same level regardless of the sucrose concentration. It also found that particle concentration was the primary factor affecting aerosol growth and amine emissions. We found an inverse relationship between aerosol particle concentration and the aerosol size, while the MEA emissions were proportional to particle concentration.

In this study, a monoethanolamine aerosol growth model was developed to investigate the aerosol growth factor. Interactions among the internal conditions in an absorber were considered in this aerosol model. Additionally, an experiment was conducted to measure aerosol particle size, for collecting in-house validation data. Sucrose was used as the aerosol nuclei instead of sulfuric acid to prevent the corrosion of equipment used in the experiment. Experimental results showed that the outlet aerosol sizes increased to the same size regardless of the sucrose concentrations. The aerosol growth model was validated using the in-house experimental data. The aerosol growth model efficiently predicted the aerosol size. For investigating aerosol growth effects, particle number concentration was determined to be the primary factor affecting aerosol growth and amine emissions. When the particle number concentration increased, the aerosol size decreased, whereas the MEA emission increased.

The separation of propylene from propane is an energy-intensive distillation process. Vapor recompression is commonly used for the separation of propylene and propane. Most studies of vapor recompression were carried out at a given pressure. It is well known that the lower the pressure, the higher the volatility difference and less energy is required to perform the separation. In a traditional column, energy of the distillation can be minimized by operating at the lowest pressure possible. The limit is usually determined by the maximum cooling capacity in the condenser, which is in turn determined by the temperature of the cooling water. Such a practice is known as floating pressure control. In a vapor-recompression column, the condenser and reboiler were replaced by a heat exchanger. Auxiliary condenser and reboiler may or may not be present. The operating constraint is determined by the anti-surge control of the compressor. Furthermore, it is necessary to ensure that the compressed vapor which acts as steam in the reboiler is not substantially subcooled to avoid the vibration of the heat exchanger. In this study, the implementation of floating pressure control for a vapor-recompression propane-propylene column with an auxiliary condenser is studied using ASPEN Plus dynamics. A control scheme that includes basic inventory control and quality control was proposed. To keep the operation within safe region of the compressor surge curve, the split of the compressed vapor going to the bottom as heating medium and passing through the auxiliary condenser is adjusted. It is shown that the column can be operated under product purity requirements of top and bottom when column pressure is reduced by 1 kg/cm2.

Hexamethylene diisocyanate (HDI) is an important chemical building block in the production of high value-added polyurethane because of its some excellent properties. Traditionally, the HDI synthesis route involves extremely toxic phosgene as an industrial scale reagent. Recently, thermal decomposition of hexamethylene-1,6-dicarbamate (HDC) has been considered as the most attractive non-phosgene process for the HDI synthesis because HDC can be synthesized by reacting 1,6-hexamethylene diamine (HDA) with a green compound, dimethyl carbonate (DMC), instead of toxic phosgene. Therefore, the intermediate HDC synthesis is crucial to the whole process. In the study, design of two plant-wide processes is explored for green HDC synthesis. In the first process, HDC is produced in two reactive distillation (RD) columns in series. Complete conversion of HDA is designed and excess DMC reactant is used to increase the reaction conversion. The mixture of un-reacted DMC and by-product methanol from RD column tops is separated by a series of conventional distillation columns containing a pre-concentrator column and an extractive distillation system comprising an extractive distillation column and a solvent recovery column. In the second process, RD is enhanced by vapor recompression (VR) to reduce energy consumption. The key cost saving from VR is attributed to fully take advantage of small temperature difference between RD column bottom and top, and higher RD column top temperature rendering condenser duty totally utilized by RD column bottom. Furthermore, additional latent heat of RD overhead vapor is released to the extractive distillation system by external heat integration, which can then achieve significant saving in the operation cost of this system. Compared to the RD intensified process, total annual cost of the RD + VR intensified process can be reduced by 35%.

this study presented a competitive electrochemical mechanism of the hydrogen and carbon monoxide to describe the real electrochemical reactions in SOFC. The SOFC models employed the competitive electrochemical mechanism was validated with a variety of fuels as the feedstocks, such as methane, mixing gas of H2 and CO, blast furnace gas (BFG), and coke oven gas (COG), via pilot-scale SOFC modules of 1 kW and 50 kW. The models were implemented on Aspen Custom Modeler platform to simulate steady-state results. Further, the SOFC of the 50 kW pilot simulation considering the whole pilot process including pre-reformers, heat exchangers, and fuel recycling. The results showed the SOFC employed the competitive electrochemical mechanism has the better performances of the power generation and voltage predictions regardless of the what fuel used in SOFC of the 1 kW scale, compared to the SOFC only employed hydrogen electrochemical mechanism. The mean voltage error of the SOFC with the competitive electrochemical mechanism using various fuels was 1.52%. the minimum error was 0.73% using COG as the feedstock. The validation of the SOFC model of the 50 kW scale showed that only a 5% error with the experiment in steady-state and 6.91% error in transition.

The regeneration energies of CO2-lean solvent and NH3-washing water comprise the main fractions of the total energy consumption in an NH3-based post-combustion carbon capture (PCC) process. However, the reconfiguration of the CO2 absorption process may significantly reduce the requirement for NH3-washing water to relieve the energy burden of regenerating it. Additionally, the utilization of a pressurized CO2 stripper to enhance the purity of CO2 in the overhead vapor is an alternative strategy for reducing the energy requirement for regenerating the CO2-lean solvent. Moreover, further energy reductions can be achieved by modifying the stripper configurations that have been reported in the literature. In this study, the energy-saving performance of the modified configurations, including the utilization of a two-stage absorber, a cold-split bypass (CSB) and an advanced flash stripper (AFS) for PCC by an NH3-based solvent is investigated. Our results indicate that the concentration of NH3 slip can be reduced to a quarter of the emission by the two-stage absorber. Moreover, this study also demonstrates that the configuration of AFS is a limiting case of the CSB modification. The energy requirement of AFS is higher than that of CSB because the heat recovery by the main heat exchanger in AFS must be sacrificed to avoid solid precipitation in the reflux.

Dilute aqueous ammonia (NH3) process is considered as one of the alternative post-combustion carbon dioxide capture (PCC) technologies. However, the energy consumption for solvent regeneration is quite high, about 0.15–0.25 MWh/ton CO2 captured as equivalent work consumption. Therefore, reducing this valuable energy duty is still the major technical challenge to extensive deployment of this PCC technology. In this work, the NH3-based carbon capture process was developed using the lean vapor recompression approach. A parametric study revealed that the optimum energy consumption occurs at the lean solvent characterized by NH3 concentration of 5.0 wt% and CO2 loading of 0.275 mol CO2/mol NH3, with the stripper and flash pressures operated at 10.50 bar and 4.17 bar, respectively. Operating under such conditions, the total work duty for a CO2 capture plant was substantially reduced to 0.087 MWh/ton CO2 captured. In this process, the need of reboiler is completely eliminated.

In chemical engineering applications, computational efficient meta-models have been successfully implemented in many instants to surrogate the high-fidelity computational fluid dynamics (CFD) simulators. Nevertheless, substantial simulation efforts are still required to generate representative training data for building meta-models. To solve this problem, in this research work an efficient meta-modeling method is developed based on the concept of transfer learning. First, a base model is built which roughly mimics the CFD simulator. With the help of this model, the feasible operating region of the simulated process is estimated, within which computer experiments are designed. After that, CFD simulations are run at the designed points for data collection. A transfer learning step, which is based on the Bayesian migration technique, is then conducted to build the final meta-model by integrating the information of the base model with the simulation data. Because of the incorporation of the base model, only a small number of simulation points are needed in meta-model training.

A novel plant-wide process is designed for epichlorohydrin synthesis by the epoxidation of allyl chloride with hydrogen peroxide in the presence of titanium silicate 1 catalyst and methanol solvent. The design targets are to satisfy the requirements of product purities and address gas-phase safety concerns caused by the exothermic and violent decomposition potential (to oxygen and water) of hydrogen peroxide. There is a risk of oxygen/allyl chloride and oxygen/methanol systems exploding in the gas phase. A plant-wide process that includes one packed bed reactor, one distillation column, one decanter, and two strippers is proposed to meet the design targets and is optimized with minimum total annual cost. The strategies of feeding excess allyl chloride into the packed bed reactor and feeding two nitrogen gas streams into the column top and gas vent of a partial condenser in the distillation column, respectively, are used to prevent explosion in the gas phase. High-purity epichlorohydrin and water can be pr...

Dimethyl carbonate is a green compound with a broad variety of application. In the study, the process design and control of synthesizing propylene carbonate (PC) for the dimethyl carbonate production by using CO2 as a raw material is investigated by indirect alcoholysis of urea. This attractive indirect alcoholysis route of urea shows many advantages such as environmentally friendly chemicals, cheap raw materials, and mild and safe operation condition. Some reaction distillation (RD)-based processes for PC synthesis by this route are proposed, designed, and optimized in this work. These processes consist of two operation configurations, near neat operation and excess reactant operation. The intensified technologies of heat integration in addition to RD are used to design economic PC synthesis processes. Steady-state simulation results indicate that the novel intensified process containing a RD column and a conventional distillation column with internal vapor compression provides the most economical design. This process is operated under excess reactant and fully utilizes the special azeotrope characteristic of propylene carbonate and propylene glycol pair. This pair forms a homogeneous minimum-boiling azeotrope near the pure PG end under low pressure. However, this azeotrope vanishes under high pressure. Furthermore, steady-state analysis is used to design a simple temperature control strategy. Different desired temperature profiles can be found in the RD column under various feed flow rates. Set point of the temperature loop for maintaining bottom product purity of the RD column is then reset when throughput rate changes. Dynamic simulation results reveal that the proposed temperature control can maintain product purities at their desired values in face of feed flow disturbances.

A systematic process intensification method via comparative grassroots and retrofit designs for distillation is proposed. Restricting to the ternary system, a case study of multicomponent C6 alkane separation in caprolactam processing is considered. The existing benchmark flowsheet of direct sequence (DS) features high economic cost but low thermodynamic efficiency. Through the investigation of the composition profile, two middle component remixing peaks and a large feed mismatch are diagnosed as the reasons of inefficiency. To achieve process intensification, potential candidates like side stream column (SSC) and dividing wall column (DWC) are enumerated and evaluated in the grassroots design step. Although DWC performs the best in thermodynamic efficiency, SSC is more preferred for retrofit as its structure is simple but has similar performance. To maximally reuse the existing equipment, two retrofit proposals of either indirect sequence (IS) or SSC are provided based on the grassroots design results. Among them, retrofitted SSC is recommended for long expected lifespan situation, while IS is more suitable for a short one. It is recommended that the proposed process intensification procedure can be extended to other similar processes or systems with more components, through enumerating and comparing the several best but similar grassroots designs with benchmark process.

Dimethyl carbonate (DMC) is a green compound with a broad variety of application. Recently, CO2-based routes to produce DMC have attracted much attention because of the environment benefits of CO2 utilization. In the study, we investigate the process design of synthesizing propylene carbonate (PC) for the DMC production using CO2 as a raw material by indirect alcoholysis of urea. The indirect alcoholysis route of urea shows many advantages because of cheap raw materials, mild and safe operation conditions, and environmentally friendly chemicals. Some different processes for PC synthesis by this route are proposed, designed, and optimized in this work. These processes can be classified in terms of two operation types: near-neat operation and excess reactant operation. Reactive distillation (RD) and heat integration technologies are used to intensify PC synthesis processes. Two processes are designed under the near-neat operation. Three RD plus conventional distillation (CD) processes with heat integration ...

Recently, reactive dividing-wall distillation (RDWD) has attracted much attention due to its capacity of process intensification. Nevertheless, its energy efficiency and economic benefit are likely to be enhanced by employing vapor recompression heat pump (VRHP) to reuse the energy of its overhead vapor. In this study, we explore the feasibility and effectiveness of this technology in two representative RDWD processes with different operation characteristics through in-depth evaluations of the steady-state performance of the VRHP reinforced RDWD ( i.e. , the proposed RDWD-VRHP). The first process involves the esterification of mixed acid (acetic acid and propionic acid) with methanol to yield methyl acetate, methyl propionate, and water. The second process involves the reaction of glycerol and hydrochloric acid to produce 1,3-dichlorohydrin, 2,3-dichlorohydrin, and water along with the intermediate products of 1-monochlorohydrin and 2-monochlorohydrin. Different configurations of the RDWD-VRHP are proposed for these two reaction systems and a systematic design procedure is developed to determine the optimum combination of the VRHP and the RDWD. Simulation results demonstrate that the RDWD-VRHP can substantially reduce utility consumption of RDWD. Especially, much more economic benefit can be secured by VRHP for the RDWD column with small column temperature difference and high latent heat in the overhead vapor.

This work evaluated the energy consumption of a large-scale post-combustion carbon capture (PCC) process for a 500 MW coal-fired power plant using aqueous ammonia solution. Firstly, the rate-based model in Aspen Plus was used to simulate the benchmarked process reported in the literature. In the benchmarked process, the ammonia stripper for the water wash loop and the carbon dioxide stripper in the carbon dioxide capture loop were designed relatively independently. The regeneration energy was reported to be 8.47 GJ/ton-CO2 using the solvent with 0.23 CO2 lean loading in mol-CO2/mol-NH3. By optimizing the benchmarked process, the optimal lean loading is about 0.20 and the regeneration energy was reported to be 6.74 GJ/ton-CO2. By integrating the ammonia stripper and carbon dioxide stripper, it was found that there is a limit on the regenerated lean loading of CO2 (∼0.15) that allows the closing of mass balance; however, the regeneration energy could be reduced to 4.0 GJ/ton-CO2.

In this study, both packed-bed (PB) and rotating PB (RPB) absorbers with a packed-bed stripper were used in a pilot plant for the removal of carbon dioxide (CO2) with 30 wt% monoethanolamine (MEA) as the solvent. The flue gas feed is the combustion product of the blast furnace gas from a steel mill containing approximately 30% of CO2. Both PB and RPB exhibit the same performance for the capture of CO2 with the same amount of energy consumption, but the RPB has a volume of approximately one-third that of PB absorber. The results obtained from experiments using ∼20 wt% MEA showed good agreement with those obtained from simulation using an Aspen rate-based model for PB and an Aspen custom modeler for RPB. These results demonstrated the feasibility of using a RPB for decreasing the size of absorber under relevant process conditions for capturing CO2.

Use of computational fluid dynamics to model chemical process system has received much attention in recent years. However, even with state-of-the-art computing, it is still difficult to perform simulations with many physical factors taken into accounts. Hence, translation of such models into computationally easy surrogate models is necessary for successful applications of such high fidelity models to process design optimization, scale-up and model predictive control. In this work, the methodology, statistical background and past applications to chemical processes of meta-model development were reviewed. The objective is to help interested researchers be familiarized with the work that has been carried out and problems that remain to be investigated.

Dichlorohydrin is an important intermediate for synthesizing epichlorohydrin, a high volume of commodity chemical largely utilized in the production of epoxy resins. In this study, a green process using an atom-efficient and environment-friendly route is used to synthesize dichlorohydrin by reacting glycerol, an available by-product in the biodiesel production, and hydrochloric acid under the homogeneous catalyst of acetic acid. A more accurate kinetic model is firstly established. Two plant-wide processes with continuous stirred-tank reactors and reactive distillation columns are then proposed and analyzed. In these processes, dichlorohydrin is obtained by decanting the overhead product of the reactive distillation column and withdrawing from organic liquid. Furthermore, some amounts of byproduct water in the reactive distillation column is withdrawn from a side stream to increase the reaction conversion. Finally, a novel intensification scheme known as thermally coupled reactive distillation or reactive divided-wall distillation is used to design the dichlorohydrin synthesis process. Simulation results demonstrate that this novel intensified plant-wide process gives much economical benefit for the dichlorohydrin production by glycerol hydrochlorination.

CoSb3-based alloys are among the most promising thermoelectric materials. The Ag-Cu eutectic alloy, Ag-39.9 at%Cu, has a melting point at 779.1 °C and is a good braze candidate for thermoelectric modules used in mid-temperatures. Since the Ag-39.9 at%Cu alloy reacts significantly with the CoSb3 substrate, the introduction of a barrier layer is necessary, and Co, Ni and Ti are potential barrier layers. This study systematically examines the reactions at the joints, Ag-Cu/Co/CoSb3, Ag-Cu/Ni/CoSb3 and Ag-Cu/Ti/CoSb3. Interfacial reactions are observed between the CoSb3 substrate and barrier layers at 450 °C and 600 °C, although they are significantly reduced with the introduction of barrier layers. CoSb2 and CoSb phases are formed at the Co/CoSb3 contact, Ni5Sb2 and (Co,Ni)Sb phases at Ni/CoSb3, and TiSb, TiSb2 and TiCoSb phases at Ti/CoSb3. After reaction at 450 °C for one day, the reaction layer thick is 2 μm, 24 μm, and 0.4 μm in the Co/CoSb3, Ni/CoSb3 and Ti/CoSb3 couples, respectively. Co has both good adhesion and low interfacial reaction rates with CoSb3 and Ag-39.9 at%Cu alloy, and it is concluded that Co is a good barrier layer at 450 °C for the CoSb3-based modules when Ag-Cu eutectic braze is used.