In the oil and gas production process, the online prediction of the oil-well production rate is an important task, that cannot only directly reflect the liquid supply capability of oil wells, but also guide the optimal control of the oil and gas production processes. However, traditional prediction methods have certain limitations in terms of accuracy and real-time properties. Therefore, to achieve an accurate prediction of the oil production rate, an adaptive integrated modeling method with a higher prediction accuracy and self-adaptability is proposed in this paper. With this method, a nonlinear mechanism model of the oil production rate is first established by analyzing the oil and gas production process and considering the nonlinear characteristics of the reservoir and multiphase flow in the wells. To reduce the influence of model parameter uncertainty and improve the prediction accuracy of the mechanism model, the least squares support vector machine (LS-SVM) method is then used to establish the error model for compensating the deviation in the mechanism model output. Moreover, to improve the adaptability of the model, an online correction strategy including a short-term correction of the LS-SVM and long-term correction of the mechanism model is proposed. Finally, through a simulation of the actual oil and gas production process in the oil production area, the results demonstrate that the proposed modeling method can not only improve the model prediction accuracy but also the model generalization, laying a solid foundation for the implementation of optimal control in the oil and gas production process.
The decomposition of a high concentration N,N-Dimethylformamide (DMF) solution by water thermal plasma was demonstrated. The result showed that DMF solutions of warying concentration (4,050–174,000 mg/L) was successfully decomposed. Decomposition rates of more than 94% was achieved, with the highest energy efficiency being 41.2 g/kWh. The temperature of the nozzle exit was measured to be 5,000–7,400 K, this extremely high temperature and O, H-radical-rich atmosphere inhibited the formation of byproducts like NOx. The major products of gas effluent are H2 (59–60%), CO (28–29%), CO2 (6–8%), and N2 (3–5%). The main form of nitrogen in the liquid effluent was nitrate nitrogen (NO3-N). Reaction pathways were proposed as follow: first, DMF dissociates into N, CH3, and CHO radicals in the arc region by electron impact; second, thermal decomposition and radical reactions with radicals like NHx and CHx occur in the plasma flame region; finally, recombination and oxidation occurs to form the products, such as N2, CO, and CO2 in the downstream region.
An orbitally shaken bioreactor is the most popular system for cell cultivation in a suspension culture owing to its unique characteristics. However, conventional bioreactors struggle to meet the anticipated demand in practical applications due to their limitations. In this study, numerical simulations are conducted to evaluate the performance of a newly developed orbitally shaken bioreactor that is equipped with a vaulted “bump” at the bottom wall. The presence of this bump significantly improves the mass transfer and cell suspension ratio in the culture medium without increasing the shear stress; it also efficiently reduces the cell aggregation in the central part at the bottom wall. In addition, to enable a suitable cell culture environment for practical applications, the Bayesian algorithm is employed to optimize the control of three parameters, namely bump height (hb), shaking velocity (ω), and shaking radius (R), of this reactor. For all the cases considered, the obtained data related to mass transfer, suspension ratio, and shear stress are analyzed. The simulation results show that the optimal bioreactor is preferable for cell cultivation compared with the initial state, owing to its high capability for mass transfer and suspending cells. It can be concluded that the methodology described in this paper is a feasible and reliable tool for performance prediction and process optimization in biotechnology.
Yield stress fluids, which show reversible gel–sol transition and a decrease in viscosity via shear, are expected for endoscopic applications. However, quantitative analyses of such fluids, including pressure drop during endoscopic catheter delivery and post-delivery dripping, have not yet been conducted from a chemical engineering perspective. In this study, we fabricated an equipment setup comprising an endoscopic catheter and a model gastrointestinal (GI) duct to which different concentrations of three model yield stress fluids, specifically, laponite (LAP), Carbopol (CP), and xanthan gum (XG), were applied and compared. We clarified the tradeoff between the pressure drop through the catheter and dripping on the GI duct model. In terms of operability, LAP performed better than CP and XG. The effect of gravity on dripping, which is greatly affected by the position of a patient, was discussed. Finally, the relationship between the operability and rheological properties such as viscosity, yield stress, and restructuring time of the three materials were quantitatively studied.
Peptides are molecules of paramount importance in several fields, especially pharmaceuticals, healthcare, and nutrition. They are mainly obtained from solid-phase peptide synthesis (SPPS). SPPS is a cyclic process with four steps in each cycle: deprotection, washing, coupling, and rewashing. As a result, SPPS typically has a long coupling time, large excesses of reagents, and many repetitive washing steps between each step. We report the development of flow peptide synthesis in a microchannel. A flow peptide synthesizer consisted of a 1-mm-wide and 0.2-mm-deep microchannel on a glass plate and a stop-cock for a resin trap. During the coupling reaction, two syringes made a reciprocating flow of resin slurry in the microchannel to restrain aggregation and blockage of resin beads. Model peptides (Met-enkephalin) were synthesized in a continuous-flow manner. The coupling time of 10 min produced improved yields and fewer side products than that of 40 min with a conventional batch synthesis. Additionally, two equivalents of amino acid gave a desirable yield similar to that of four equivalents of amino acid in the microchannel. Therefore, the flow peptide synthesis in a microchannel can contribute to significantly reducing the total coupling time and consumption of amino acid.
Most microcapsule preparation methods produce a population of microcapsules in a bulk solution. To control the microcapsule preparation or obtain an optimal preparation condition, the mechanism of the microcapsule preparation should be investigated. The mechanism is estimated via structure reformation during the preparation process because diameter and wall thickness are drastically altered in the solution. Considering microcapsule applications, some important properties, such as the mechanical properties of microcapsules and release rate of the encapsulated product, depend on the microcapsule structure. In this study, polystyrene microcapsules containing saline water droplets were prepared via the solvent evaporation method from a solid-in-oil-in-water (S/O/W) emulsion system. The microcapsules exhibited a specific structural distribution, which comprised monocore, multicore, and solidcore structures. The structural distribution was altered by the preparation condition. The monocore structure was absolutely dominant owing to the increase in the amount of calcium chloride added in the organic phase. The salt concentration is not the sole controlling factor of the microcapsule structure, as the surfactant and dispersion exerted a significant impact on the microcapsule structure. The structural distribution was automatically analyzed by a machine learning algorithm (MLA). The decision-making time for the microcapsules preparation was shortened by the accelerated structure determination, and the accuracy was improved by increasing the number of counting particles.