On some reduced order models for packed separation processes

The use of packed-bed separation columns as a liquid-gas contact system in absorption and distillation has steadily increased in the chemical and process industries and with it the need for tools for their adequate design and control. The mathematical models for packed separation columns are known for their large dimensionality. This can pose a problem when one considers the design and/or optimisation of systems involving more than one column or a single large column. The use of reduced-order models came as an answer to this problem. Reduced-order models presented to date do not rigorously solve the mass transfer subproblem. Four generalised steady-state reduced-order models for separation processes in packed columns are developed and compared in this work. The models are based on the two film theory of mass transfer and the more rigorous of them have as a starting point one of the so called rate based methods. The mass and energy transfer rates across the vapour liquid interface are evaluated by means of different approximate solutions of the Maxwell-Stefan equations for steady-state, unidirectional mass transfer. The differential equations of the models are converted into algebraic equations through the application of the orthogonal collocation procedure on the spatial variable. The resulting system of algebraic equations is subsequently solved using a modification of the Powell hybrid method. Three case studies dealing with distillation columns are presented but the models are easily modified to work with other separation processes (e.g., absorption). The results of the simulations indicated a clear advantage when using more rigorous methods for the computation of the interphase mass transfer rates. Their inclusion in the reduced-order models improved the convergence characteristics of the solution with respect to the number of collocation points and also increased the robustness of the models in converging towards the solution. These improvements were obtained without increasing significantly the time spent in the simulations when compared with a model using an effective diffusivity approach in the evaluation of the mass transfer rates

Numerical Investigation Of Spray Characterization Of Heater-GDI System

Gasoline engines require fuel enrichment at low temperature cranking (cold start) conditions for efficient engine operation. Since the amount of fuel injected is high at cold start to compensate low fuel evaporation, fuel sprays tend to impinge on the cold surfaces of the piston and cylinder walls leading to the formation of excessive unburned hydrocarbons. One of the ways to ensure reliable cold start performance and to reduce UHC emissions is to have the fuel subjected to preheating. The objective of this study is to investigate the effects of fuel preheating on Gasoline Direct Injection sprays to improve the mixture preparation during the cold start conditions. Injection of fuel sprays of neat gasoline and gasoline-ethanol blends (E10 and E85) from the heater-injector into a constant volume combustion chamber was studied. Computational Fluid Dynamics (CFD) simulations of the fuel flow through the injector were performed to understand the impact of the heater in improving the mixture preparation quality. The gasoline was modelled with 14 component surrogate fuel model to capture its physical properties and distillation characteristics. Fuel spray processes in the Constant Volume Combustion Chamber (CVCC) were simulated using an in-house CFD code, MTU-KIVA. Parametric simulations were performed at different injection pressures and at a wide range of fuel temperature ranging from -6°C to 250°C. The simulation of injector internal flow could improve the spray simulations in the CVCC by providing accurate velocity and temperature distributions of fuel sprays at the exit of individual nozzles. The results show that the injector with the preheating system performs reliably at cold start conditions to increase the fuel temperature from -6°C to 75°C in less than a second. The spray were in good agreement between the measurements and predictions. For the given operating range, the spray changes from normal evaporation to flash boiling regime. The model captures the spray collapsing behaviour for the flash boiling conditions. However, the model tends to over-predict the spray penetration for fuel temperatures in the higher range of boiling/flash boiling, regardless of the injection pressure variation

Monitoring and control for NGL recovery plant

The thesis explores the production of natural gas liquids (NGL) and the challenge of monitoring and controlling the fractionation process. NGLs are the C2+ hydrocarbon fraction contained in natural gas, which includes useful feedstocks for industrial production processes. Since NGLs have greater economic value compared to natural gas, their recovery has become increasingly economically significant, leading to a need for efficient fractionation. This energy-intensive process is typically conducted in separation trains that include cryogenic distillation columns. Given the high cost of composition analyzers and the related significant delays, this work proposes the use of only indirect composition control strategies, as well as data-driven control strategies to achieve the desired product quality and optimize the plant energy consumption under typical disturbances. Feedforward neural networks (FFNs) were used for the development of soft sensors used in data-driven control schemes. Given the multitude of data made available by the process simulator, this work aims to develop a demethanizer digital twin that can approximate the column dynamics with reduced computation time. Long Short-Term Memory neural networks (LSTM), along with physical knowledge, were used to develop different neural network architectures compared to select the most suitable for the surrogate model development. Realistic measurement noises were considered to accurately reflect the measurements of real industrial plants and only easy-to-measure variables were used as input data for the developed neural model. Overall, the research presents an energy-efficient NGL recovery offering a cost-effective and efficient alternative to traditional measuring instruments. Moreover, the study illustrates a novel application of LSTM for distillation columns digital twins realization, providing a useful tool for optimization, monitoring and control by employing available plant measurements

Nonlinear Model Predictive Control Of A Distillation Column Using Hammerstein Model And Nonlinear Autoregressive Model With Exogenous Input.

Turus penyulingan adalah unit proses penting dalam industri penapisan petroleum dan kimia. Ia perlu dikawal hampir dengan keadaan-keadaan pengendalian yang optima demi insentif- nsentif ekonomi. Distillation column is an important processing unit in petroleum refining and chemical industries, and needs to be controlled close to the optimum operating conditions because of economic incentives

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

The determination of plate efficiencies in unsteady-state plate-column models

Other investigations have shown the relationship between various plate efficiencies and the relationship between the Murphree plate efficiency and the point efficiency for steady-state conditions. So far there has been no work done on the relationship between the Murphree plate efficiency and the point efficiency for unsteady-state conditions. In unsteady-state distillation simulation, the Murphree plate efficiency has been held constant and it is thought that this may be the reason for the differences between experimental and theoretical liquid composition responses in some cases. Further, the liquid mixing models used to represent the mixing occurring on a distillation plate, do not include down-comer sections and the need for experimental investigation of the down-comer effect is required to produce a realistic model. Experimental conductivity impulse responses, using potassium chloride tracer, are obtained on a 7 ft. x 1.5 ft. sieve plate using the system air-water. The sieve trays, weir height and down-comer segmental area are variable, and the responses are compared with those obtained by the diffusion model. [Continues.

Modeling of Multicomponent Multistage Separation Processes

Chemical Engineerin

Novel Formulation and Application of Model Predictive Control.

Model predictive control (MPC) has been extensively studied in academia and widely accepted in industry. This research has focused on the novel formulation of model predictive controllers for systems that can be decomposed according to their nonlinearity properties and several novel MPC applications including bioreactors modeled by population balance equations (PBE), gas pipeline networks, and cryogenic distillation columns. Two applications from air separation industries are studied. A representative gas pipeline network is modeled based on first principles. The full-order model is ill-conditioned, and reduced-order models are constructed using time-scale decomposition arguments. A linear model predictive control (LMPC) strategy is then developed based on the reduced-order model. The second application is a cryogenic distillation column. A low-order dynamic model based on nonlinear wave theory is developed by tracking the movement of the wave front. The low-order model is compared to a first-principles model developed with the commercial simulator HYSYS.Plant. On-line model adaptation is proposed to overcome the most restrictive modeling assumption. Extensions for multiple column modeling and nonlinear model predictive control (NMPC) also are discussed. The third application is a continuous yeast bioreactor. The autonomous oscillations phenomenon is modeled by coupling PBE model of the cell mass distribution to the rate limiting substrate mass balance. A controller design model is obtained by linearizing and temporally discretizing the ODES derived from spatial discretization of the PBE model. The MPC controller regulate the discretized cell number distribution by manipulating the dilution rate and the feed substrate concentration. A novel plant-wide control strategy is developed based on integration of LMPC and NMPC. It is motivated by the fact that most plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems. LMPCs and NMPCs are applied to the respective subsystems. A sequential solution algorithm is developed to minimize the amount of unknown information in the MPC design. Three coordination approaches are developed to reduce the amount of information unavailable due to the sequential MPC solution of the coupled subsystems and applied to a reaction/separation process. Furthermore, a multi-rate approach is developed to exploit time-scale differences in the subsystems

Advances in simulated moving bed : new operating modes : new design methodologies and product (FlexSMB-LSRE) development

Tese de doutoramento. Engenharia Química e Biológica. Faculdade de Engenharia. Universidade do Porto. 200