Nikolai A. VoinovDenis A. ZemtsovNina V. Deryagina
13页
查看更多>>摘要：This paper examines diabatic columns with a new type of tray. The design provides effective liquid reflux flow in each column tray and a low pressure drop. The contact devices comprise a stack of multi-diameter plates with raised edges. The vertical tubes of the dephlegmator integrated into the column centre pass across all plates, and are used for heat removal. Vapors of the ascending mixture partially condense onto the plate surfaces and the dephlegmator, resulting in vapor enrichment. The liquid flow down the column forms a thin layer on the surfaces of the middle and bottom plates. When the condensate drops onto the smaller-diameter top tray, it partially evaporates, is then mixed with the liquid flow on the underlying plates, and partially evaporates again. An algorithm calculation of distillation parameters for a diabatic column, with a known reflux flow rate, vapor velocity and heat transfer coefficient, is proposed and approbated. The column separation efficiency is assessed depending on the temperature and cooling-water flow rate in the dephlegmator pipe. Several approaches for enhancing the heat transfer intensity in the column dephlegmator pipe were analysed. Several heat removal methods, including liquid film flow cooling of the inner dephlegmator pipe surface, were examined. Experimental tests were carried out using an ethanol-water binary mixture and a raw spirit complex mixture. Comparison of the characteristics of the new diabatic unit and a packed column with a spiral prismatic packing showed that the plate column with the same separation efficiency has a lower pressure drop and metal consumption. The results encourage further research on this new diabatic column design.
查看更多>>摘要：A catalytic reaction process for producing methanol from carbon dioxide and hydrogen gases has been suggested and simulated. However, there can exist parametric uncertainties on the process model such as reaction kinetics. A reactor model considering parametric uncertainty results in a distributional process output and it can give more informative data compared to the conventional modeling methods which use a single parameter set. However, the distributional model needs a lot of computational loads because of the Monte Carlo simulation and iterative calculations for convergence. In order to alleviate the heavy computational load and reflect the skewness of the distributional data, generalized extreme value distribution and neural network technique are utilized. The formation parameters of generalized extreme value distribution are learned by shallow and deep structured neural network and as a result distributional reactor model in an explicit formulation is proposed. Compared to the result using shallow structured neural network for learning the formulation parameters, that using deep neural network shows improved predictive performance especially adjacent to the boundary layers of process inputs. The proposed model can be utilized to real-time stochastic model based approaches in optimization and control with less computational load because of its explicit and distributional formulation.
查看更多>>摘要：Intermittent manufacturing is becoming increasingly popular due to its capability of coping with dynamic changes in market demands. The operation of the intermittent manufacturing equipment is subject to frequent restarts as the type of product being produced is switched, and it is challenging to achieve consistency in production during the start-up phase when restarts take place. Timely identification and monitoring of this phase is critical for avoiding the waste of materials and improving the product quality for intermittent manufacturing. In this work, a hierarchical stationarity analysis is proposed for the monitoring of the start-up phase process operation of intermittent manufacturing to extract two types of stationary information. First, consistently invariant process variations among batches are separated using a Kullback-Leibler divergence-based feature extraction method. In this way, process variations in each batch are divided into two subspaces - the stationary subspace and the remaining non-stationary subspace. Cointegration analysis is performed on the non-stationary subspace to capture the stationary information to find the long-term equilibrium during the start-up phase. Based on the divided subspaces, the start-up phase is identified, and process abnormalities can be online monitored. The efficacy of the proposed method is illustrated through a plastic molding process.
查看更多>>摘要：Extractive coupled oxidative desulfurization (ECODS) is a promising technology for efficient deep desulfurization of fuels with integrated product separation. The main objective of this contribution is the development of an efficient downstream process for the separation and recovery of the molecular HPA-5 (H8PV5Mo7O_(40)) catalyst by means of nanofiltration membranes. We obtained optimized parameter settings for the membrane separation process using a Box, Hunter & Hunter design of experiments leading to higher rejection of catalyst components above 99%. The optimized parameters were successfully applied for recycling experiments in ECODS. In detail, model gasoline consisting of ben-zothiophene in iso octane could be efficiently desulfurized in six consecutive runs with product separation by the means of nanofiltration between the individual runs. Distinct changes of the catalyst structure were indicated by ~(51)V NMR and ~(31)P NMR spectroscopy after the second recycling step due to acidification of the aqueous reaction solution. Based on these results, we predict that a continuous process with a coupled nanofiltration separation could push the ECODS technology to industrially relevant technology readiness levels.
查看更多>>摘要：Based on detailed experiments with optical shadowgraphy in a bubble column, the influence of liquid viscosity on bubble behaviour was numerically computed with the Euler/ Lagrange approach including mass transfer. Fluid flow and sub-grid-scale-turbulence (SGS) was obtained by Large Eddy Simulation (LES) including a k-transport equation. Bubbles were tracked by a point-mass approach, considering all relevant forces. For the lift coefficient a composite correlation was applied considering viscosity. An experimentally-based bubble dynamics model was considered, randomly generating eccentricity, motion angle and Sherwood numbers. SGS turbulence acting on bubble motion was modelled through a Langevin-type approach. Two-way coupling was considered in momentum and SGS turbulence. The bubble column (diameter 140 mm;; height 710 mm) contained either pure water or water-glycerol mixtures. Bubbles were injected through 4 needles yielding a distribution with a mean diameter 3.6 mm. The numerical results demonstrated the enormous effect of accounting for bubble dynamics and the possibility to adapt this model for different liquid properties, although not yet generalised. Overall, good agreement between simulations and measurements was found for non-reactive and reactive cases. This concerns local properties, spatial distributions of the velocities of both phases and the temporal evolution of the pH-value when modelling bubble dynamics properly.
查看更多>>摘要：This work deals with the modeling, computer simulation, validation, and exergy evaluation of a ～40 ton/h industrial air separation plant located in Colombia. The model was implemented in Aspen Hysys~R at the corresponding operating conditions of the facility. Initially, the reliability of the model was verified by comparison with data reported in the plant manual (EDM) from the base operating point. An additional validation was performed by comparison with historical records which involved a statistical analysis of plant metadata. The model showed good agreement with both sets of data, displaying deviations < 10%, and it was used to identify that compressors (1325 kW), multichannel heat exchanger (519 kW) and the high-pressure column (448 kW) exhibit the largest exergy destruction rates. By mean of a sensitivity analysis, main controlled variables affecting the process performance were identified. This enabled to detect the feasible operating zones, and a local minimum of exergy destruction for each variable. The overall exergy efficiency was estimated in 0.18, and the specific power consumption with respect to produced oxygen was 1.26 kWh/Nm~3 O2. Results indicate that plant performance needs to be highly improved to reduce energy intensity and that the obtained model is suitable for further optimization.
查看更多>>摘要：With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO2) has received increasing attention as an alternative source of carbon-based fuels. As CO2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor;; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Lastly, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO2.
David PiconNicolas TorassoJose Roberto Vega Baudrit
11页
查看更多>>摘要：Arsenic is a concern for its ubiquity in the environment and its accumulative and toxic properties. Water is often contaminated with this chemical, so developing simple, scalable, and green water treatment technologies is urgently needed. We show here that the ability of the L-Cysteine biomolecule to form complexes with arsenic inspires its use as a natural bio-inspired sorbent to develop advanced functional materials. We establish for the first time a way to chemically anchor L-Cysteine (L-Cys) inside highly hydrophilic nanofibers to create a membrane capable of lowering As(V) concentration below the WHO limit of 10 μg/L. A homogeneous precursor mixture of an aqueous solution of PVA and l-Cys (5 wt% and 10 wt% of L-Cys with respect to PVA) was electrospun to obtain a nano-fibrous membrane. Successful immobilization of L-Cys within PVA nanofibers is achieved during heat treatment at 190 °C. It occurs through esterification reactions between the hydroxyl group on the PVA chain and the carboxylic acid on L-Cys. Arsenic sorption (as As (V)) was assessed by batch experiments in aqueous media and at a controlled pH range. The maximum removal efficiency was achieved at pH 7, supporting the formation of thiolate complexes as the primary mechanism for arsenic sorption. We show that L-Cys confinement makes arsenic diffusion inside the nanofibers a rate-limiting process in adsorption kinetics, following the pseudo first order equation. Overall, this work establishes a novel arsenic remediation strategy and encourages the research of nature-mimicking adsorbents and biodegradable polymers to develop functional materials in water remediation.
查看更多>>摘要：Data-driven method has been widely used in Fluid Catalytic Cracking (FCC) process modeling. However, due to the complexity of chemical process both in time and spatial domain, how to reflect the time and spatial characteristics of FCC units and build corresponding model is important to construct a better model for the gasoline yield prediction. In this paper, a special neural network structure was developed to deal with the input variables with different time scales considering the collection characteristics of various variables, as well as the time continuity of large-scale process manufacturing units, LSTMs with different time scales are stacked to extract temporal and spatial features to help capture the relationship between influencing factors and product yield. The characteristics of FCC process are also fully reflected in data processing and building model. It is demonstrated from the conclusions that the new model developed in this paper performs better than the traditional LSTM networks, which will be of great help to the intelligent upgrading of the FCC process.
查看更多>>摘要：Considering the economic and environmental importance of bioethanol fuel, this research evaluated the energetic, economic, qualitative and environmental aspects of the conventional purification system and also of two alternatives aimed at eliminating secondary flows and reducing costs associated with utilities and operation. Using the Aspen Plus software with the insertion of a relevant number of components of the industrial process, the purification of hydrated bioethanol was investigated, optimizing the specifications of the distillation set. The conventional process (C), composed of columns A and B and condensers RR1 and EE1E2, is subdivided into sections AA1D and BB1, respectively. In turn, the alternative systems (I & II) are similar to process C but without section D of column A;; in the first system without the condensers R and the second including R1. These processes were satisfactorily developed and in compliance with the quality standards of specific Brazilian legislation. From the energy, economic and environmental point of view, the alternative processes lead to better results than the conventional system, especially alternative I, which provided reductions of 11.5 %, 6 % and 7.5 % in terms of steam consumption, operating costs and CO2 emissions, respectively. Although the application of the conventional process can be used to comply with the biofuel standard, simplified industrial plants, such as those proposed in the alternative processes, are sufficient and their use should be encouraged to extend the application to more types of products.
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