The self-diffusion coefficients of three solutes were measured in poly(methyl methacrylate) (PMMA) swollen by supercritical carbon dioxide (scCO2) for fundamental research on supercritical impregnation technology. Anthraquinone, 2-methyl anthraquinone, and disperse red 1 were selected as the solutes. UV-visible spectrophotometry was used to measure the absorbance of the solutes impregnated into the PMMA film. Fick’s second law of diffusion was applied to calculate the diffusion coefficients of the solutes. The diffusion coefficients were measured in the range 10–21 MPa and 313.2–333.2 K. The experimental diffusion coefficient data for the solutes are correlated well with the free volume theory.
A non-isothermal kinetic model is proposed to study the selective catalytic reduction (SCR) of NH3 over the Cu-ZSM-5 monolithic catalyst for improving the reduction efficiency of the NO gas exhausted from an internal combustion engine. In the kinetic model, the coupling of the chemical reaction, fluid flow, mass transfer, and heat transfer is considered for the numerical analysis. The model is solved by the finite element method. In this study, a simulation is conducted to verify the maththeoretical model. In the simulation, a three-dimensional non-isothermal model is adopted under different conditions of the gas temperature. The comparison between the simulated and the measured results reveals that the proposed non-isothermal kinetic model is accurate and reliable for analyzing the SCR process. From the simulated results, we found that the reduction efficiency of NO is significantly promoted by a high NH3/NO ratio in the high gas-temperature range. Whereas in the low gas-temperature range, the reduction efficiency of NO is affected by a low ambient temperature. The present study could provide theoretical guidance for the parametric optimal study of the NH3-SCR process to improe the reduction efficiency of NO.
A protocol for the detachment of solid samples deposited on flat substrates and their collection in aqueous samples is proposed based on a suspension-assisted ultrasonic method. As samples, combustion-synthesized magnesium oxide aggregates in the submicron size range were deposited in the gas phase onto three kinds of substrates: a silicon wafer and coarse and fine alumina-coated resin sheets. To enhance the sample particle detachment, a solid–liquid suspension made of candle combustion soot particles was selected as an ultrasound propagation medium, which is different from the usual liquid medium, such as water, a surfactant solution, or a solvent. Preliminary detachment experiments were performed using low-power (42 kHz and 35 W) ultrasonication, and the substrates and suspensions were analyzed using scanning electron microscopy (SEM) images and particle size distributions based on dynamic light scattering, respectively. The detachment efficiency, defined as the fraction of cleaned area on a substrate, was determined from the SEM images and indicates that the detachment using the medium with soot had a higher efficiency compared to that without soot, and there was an optimum soot concentration for particle detachment for all three substrates. The suspension particle size distribution after ultrasonication showed good dispersion of the sample particles in the soot suspension.
Batch process quality prediction has broad application prospects in manufacturing and chemical industries. However, during the final quality prediction of a batch process, the final target values may be related to the whole process track of the batch reaction. Thus, the final quality prediction problem embraces complex high-dimensional input and simple low-dimensional output, which also means a serious size mismatch between input data and predictive values. Motivated by these difficulties, a hybrid prediction model is proposed, which combines the advantages of stacked auto-encoder (SAE) and bi-directional long short-term memory (BLSTM) for the final quality prediction of a batch process. The feature extraction ability of SAE is used to obtain the low-dimensional features of historical process data along the time direction. Then, the validity of the framework was verified by taking penicillin fermentation as an example.
This paper presents a prototype method that can support the selection of interventions for enhancing medication adherence in medicine taking processes. The intended users are qualified practitioners, such as physicians and pharmacists, who prescribe oral medicine to patients. We performed an experimentation involving 54 university students and nine interventional tools, and multiobjective evaluation regarding the effectiveness and effort required. Using the results from this focused group, the prototype method was developed in a form of guidance. The target patients are those with the ability to self-administer medications like the participants. The method first classifies the patients according to the attitude towards medication adherence and the type of predictor for nonadherence, and then helps to choose an appropriate intervention for the patient. The developed prototype serves as a first systematic guidance for supporting such decision-makings in the clinical practice. The study motivates the application of process systems approaches to the use phase of drugs.
The floating storage regasification unit (FSRU) process has been designed & constructed as modules to achieve the fastest delivery and the easiest installation of an offshore liquefied natural gas (LNG) project. Project efficiencies, including the cost of handling materials, minimization of project delays, and avoidance of bottlenecks require the use of an appropriate module layout in the engineering phase. We present a new framework for the module layout optimization problem in the FSRU process, considering the risk, operation, and maintenance of the process module in a limited area. The developed model aims to minimize the cost of the piping connected between the modules considering both the safety and economy of the process against fire and explosion scenarios. In addition, a quantitative risk assessment (QRA) study was conducted using individual risk indices for determining the risk-avoiding safety distance between the module and the control bridge to evaluate the risk associated with the LNG regasification process. Moreover, a case study was conducted on the conceptual design layout to illustrate the applicability of the proposed model on an FSRU that can process 1,000 million standard cubic feet per day. Overall, the developed model suggested safety guidelines for the operation and maintenance of the optimal module layout in case of fire & explosion accident.
Accurate state estimation is critical for the management of zinc–nickel single-flow battery (ZNB) stack energy storage systems. The parameters of typically used models are primarily obtained via empirical or offline identification. Hence, the dynamic property of the parameters results in an inaccurate condition monitoring of these models. Therefore, a recursive least squares with forgetting factor algorithm and an unscented Kalman filter algorithm are adopted simultaneously to realize online parameter identification and state estimation. Verification results show that the joint algorithm can accurately capture the dynamic characteristics of the model parameters and exhibits high accuracy and robustness in estimating the capacity and state of charge. To further investigate the potential of ZNBs, the online tracking of peak power is realized by the constant current–constant voltage hybrid mode based on the model above and by considering the design current and cutoff voltage limits. A novel pulse experiment is designed to verify the peak power model, and the results show that the model accuracy satisfies engineering requirements.