Decision support for the development,simulation and optimization of dynamic process models

Simulation is besides experimentation the major method for designing,analyzing and optimizing chemical processes.The ability of simulations to reflect real process behavior strongly depends on model quality.Validation and adaption of process models are usually based on available plant data.Using such a model in various simulation and optimization studies can support the process designer in his task.Beneath steady state models there is also a growing demand for dynamic models either to adapt faster to changing conditions or to reflect batch operation.In this contribution challenges of extending an existing decision support framework for steady state models to dynamic models will be discussed and the resulting opportunities will be demonstrated for distillation and reactor examples.     

A Simple technique for microfluidic heterogeneous catalytic hydrogenation reactor fabrication

A simple process is described for the facile production of microfluidic reactors with ‘built-in’ metallic catalysts. This approach provides considerable reductions in cost and complexity when compared to existing catalytic chip designs. The process involves the sputtering of catalytic metals into the channels of microfluidic reactors prior to device bonding. The utility of such microreactors as environments for heterogenous catalytic hydrogenations was tested and demonstrated by applying them to the on-chip reduction of butyraldehyde to butanol and benzyl alcohol to benzaldehyde. The use of such ‘built-in’ systems as microreactors for specific processes was shown to have considerable potential for both fundamental research and industrial application.     

Modelling of homogeneously catalysed reactive distillation processes in packed columns: Experimental model validation

The design of reactive distillation processes requires reliable and accurate models to significantly decrease the expensive and time consuming experimental work. Different modelling approaches of varying complexity are available in the open literature. However, only few publications exist in which the question of the optimal modelling depth is discussed for homogeneously catalysed processes. Unlike these publications, we used experimental data in the present study to compare them with simulation results using different modelling depths for homogeneous reactive distillation processes. The nonequilibrium-stage model using the Maxwell–Stefan equations, the nonequilibrium-stage model using effective diffusion coefficients, the equilibrium-stage model including reaction kinetics, and the equilibrium-stage model assuming chemical equilibrium were investigated. The homogeneously catalysed transesterification of dimethyl carbonate with ethanol, for which pilot-scale experimental data were available, was used as a test system.     

Grey-box modelling and control of chemical processes

The success of model based control of chemical processes is dependent on good process models. Many of these processes exhibit strong nonlinearity and time varying parameters and are often difficult to model accurately. The ‘grey-box’ model which combines partial knowledge of the process, with a neural network to capture the remaining dynamics, is a promising modelling tool for nonlinear processes. This modelling methodology maximizes the use of a priori process knowledge. This, in turn, reduces the size of the neural network required to capture the remaining dynamics, hence, less data for training and faster convergence can be achieved. The grey-box model is combined with a generic model control structure and applied to a number of simulations as well as a real-time process.     

Dynamic modelling of a carbon-in-leach process with the regression network

The regression network provides a connectionist framework in which both parametric and non-parametric modelling can be implemented. It is shown how mechanistic knowledge can be built directly within the connectionist structure that results in a semi-empirical network model. In doing so the inherent freedom of a specific model is restricted so that the generalisation performance of such a model improves accordingly. It is described how a semi-empirical regression network kinetic model is developed for the dynamic modelling of the carbon-in-leach (CIL) process for gold recovery. By providing for mechanistic knowledge in the connectionist structure and catering for poorly understood aspects of the process by use of non-parametric regions within the structure of the semi-empirical regression network, the regression network kinetic model displayed significant superiority in generalisation properties over other non-parametric regression models if evaluated during dynamic simulation runs.     

Batch-to-batch optimal control of a batch polymerisation process based on stacked neural network models

A neural network based batch-to-batch optimal control strategy is proposed in this paper. In order to overcome the difficulty in developing mechanistic models for batch processes, stacked neural network models are developed from process operational data. Stacked neural networks have enhanced model generalisation capability and can also provide model prediction confidence bounds. However, the optimal control policy calculated based on a neural network model may not be optimal when applied to the true process due to model plant mismatches and the presence of unknown disturbances. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch-to-batch optimal control strategy based on the linearisation of stacked neural network model is proposed in this paper. Applications to a simulated batch polymerisation reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances.     

New automatic control strategies for sludge recycling and wastage for the optimum operation of predenitrification processes

Dynamic modelling and simulation is increasingly being employed as an aid in the design and operation of wastewater treatment plants (WWTPs). In this paper a simulation model is used to investigate the control of an activated sludge process. Two different control strategies to optimise nitrogen removal in a predenitrification process are presented, which are based on the control of the sludge blanket height (SBH) or sludge age (sludge retention time, SRT) and mixed liquor suspended solids (MLSS) in the reactor, by adjusting the sludge wastage and sludge recycle flow rates, respectively. It is shown how the recycle and wastage flow rate variables can be employed to continually maintain plant operation in the presence of disturbances. The performance of the control strategies has been assessed by the COST 624 benchmark plant and a pilot plant. The results have shown the robustness of the implementation and the efficiency of the proposed control strategies, which have been operated automatically in a safe, stable and optimum operating point, improving effluent quality and reducing energy costs. Consideration is also given as to how plant operation could be optimised in the long‐term in order to meet the yearly total nitrogen standard. Copyright © 2005 Society of Chemical Industry     

Ultrasonic Fabrication of Polymer Plate Reactors with a Surface Coating

Microreactors offer several advantages compared to the industrial scale when developing new chemical processes. Especially the production and investment costs are low if polymer microreactors are generated by ultrasonic processes. In order to observe the chemical reaction and the flow configuration, these microreactors need to be optically transparent, mechanically stable, and chemically inert to several reagents. The manufacturing process of a transparent polymer plate reactor with a chemically inert surface coating by ultrasonic fabrication is described. Experimental characterization of the microreactors showed that they are leak tight up to a pressure difference of at least 300 kPa and the mixing times are in the range of milliseconds.     

Nanofluids Research: Key Issues

Nanofluids are a new class of fluids engineered by dispersing nanometer-size structures (particles, fibers, tubes, droplets) in base fluids. The very essence of nanofluids research and development is to enhance fluid macroscopic and megascale properties such as thermal conductivity through manipulating microscopic physics (structures, properties and activities). Therefore, the success of nanofluid technology depends very much on how well we can address issues like effective means of microscale manipulation, interplays among physics at different scales and optimization of microscale physics for the optimal megascale properties. In this work, we take heat-conduction nanofluids as examples to review methodologies available to effectively tackle these key but difficult problems and identify the future research needs as well. The reviewed techniques include nanofluids synthesis through liquid-phase chemical reactions in continuous-flow microfluidic microreactors, scaling-up by the volume averaging and constructal design with the constructal theory. The identified areas of future research contain microfluidic nanofluids, thermal waves and constructal nanofluids.     

搅拌槽内三维流场的数值模拟

应用商业计算流体力学软件CFX对搅拌槽内的流场进行了模拟,并与PIV测试结果进行了比较,流型吻合良好.速度分量的对比结果表明不同情况与各种模型的吻合情况不尽相同,标准k-ε双方程模型、RNG k-ε模型和代数应力模型在主流域内都能较准确地模拟搅拌槽内的流动场.     

NONLINEAR MODEL PREDICTIVE CONTROL

Nonlinear Model Predictive Control (NMPC), a strategy for constrained, feedback control of nonlinear processes, has been developed. The algorithm uses a simultaneous solution and optimization approach to determine the open-loop optimal manipulated variable trajectory at each sampling instant. Feedback is incorporated via an estimator, which uses process measurements to infer unmeasured state and disturbance values. These are used by the controller to determine the future optimal control policy. This scheme can be used to control processes described by different kinds of models, such as nonlinear ordinary differential/algebraic equations, partial differential/algebraic equations, integra-differential equations and delay equations. The advantages of the proposed NMPC scheme are demonstrated with the start-up of a non-isothermal, non-adiabatic CSTR with an irreversible, first-order reaction. The set-point corresponds to an open-loop unstable steady state. Comparisons have been made with controllers designed using (1) nonlinear variable transformations, (2) a linear controller tuned using the internal model control approach, and (3) open-loop optimal control. NMPC was able to bring the controlled variable to its set-point quickly and smoothly from a wide variety of initial conditions. Unlike the other controllers, NMPC dealt with constraints in an explicit manner without any degradation in the quality of control. NMPC also demonstrated superior performance in the presence of a moderate amount of error in the model parameters, and the process was brought to its set-point without steady-state offset.     

BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.     

BATCH-TO-BATCH OPTIMAL CONTROL OF BATCH PROCESSES BASED ON RECURSIVELY UPDATED NONLINEAR PARTIAL LEAST SQUARES MODELS

A batch-to-batch optimal control approach for batch processes based on batch-wise updated nonlinear partial least squares (NLPLS) models is presented in this article. To overcome the difficulty in developing mechanistic models for batch/semi-batch processes, a NLPLS model is developed to predict the final product quality from the batch control profile. Mismatch between the NLPLS model and the actual plant often exists due to low-quality training data or variations in process operating conditions. Thus, the optimal control profile calculated from a fixed NLPLS model may not be optimal when applied to the actual plant. To address this problem, a recursive nonlinear PLS (RNPLS) algorithm is proposed to update the NLPLS model using the information newly obtained after each batch run. The proposed algorithm is computationally efficient in that it updates the model using the current model parameters and data from the current batch. Then the new optimal control profile is recalculated from the updated model and implemented on the next batch. The procedure is repeated from batch to batch and, usually after several batches, the control profile will converge to the optimal one. The effectiveness of this method is demonstrated on a simulated batch polymerization process. Simulation results show that the proposed method achieves good performance, and the optimization with the proposed NLPLS model is more effective and stable than that with a batch-wise updated linear PLS model.     

Tendency modeling of semibatch reactors for optimization and control

Significant economical benefits can be derived from the optimal operation of semibatch reactors even when the detailed understanding of the chemical kinetics is not at hand. To realize these economical benefits a general adaptive optimal control strategy is needed that properly accounts for the lack of detailed knowledge and the great variety of semibatch processes and at the same time takes advantage of the plant data collected during the process operation.In the present paper a novel approach is introduced for the systematic development of approximate reaction networks that are the basis of the reactor model. Other simple kinetic models can be postulated on intuitive grounds. All such networks lump the known species as reactants, product and byproduct and along with a set of mass and energy balances for semibatch reactors constitute the tendency model.This modeling approach is applied to a complex kinetic network containing 20 parameters which is accurately approximated by two simple tendency models that have either 4 or 6 parameters related to the kinetic constants. Estimation of these parameters shows the accuracy of the derived models to be very satisfactory.     

A Review of Microfluidic Experimental Designs for Nanoparticle Synthesis

Microfluidics is defined as emerging science and technology based on precisely manipulating fluids through miniaturized devices with micro-scale channels and chambers. Such microfluidic systems can be used for numerous applications, including reactions, separations, or detection of various compounds. Therefore, due to their potential as microreactors, a particular research focus was noted in exploring various microchannel configurations for on-chip chemical syntheses of materials with tailored properties. Given the significant number of studies in the field, this paper aims to review the recently developed microfluidic devices based on their geometry particularities, starting from a brief presentation of nanoparticle synthesis and mixing within microchannels, further moving to a more detailed discussion of different chip configurations with potential use in nanomaterial fabrication.     

A pore-scale model for microfibrous ammonia cracking microreactors via lattice Boltzmann method

Microfibrous microreactors with high reactive surface-to-volume ratio are good choices for ammonia cracking, which is one of the main strategies for CO-free hydrogen production. In the current study, a numerical model based on the lattice Boltzmann method (LBM) is presented to investigate ammonia cracking microreactors with coupled physiochemical thermal processes at the pore scale. Several sets of transport phenomena such as fluid flow, species transport, heat transfer and chemical reaction are taken into account. Moreover, to model the species transport in the ammonia cracking microreactor an active approach is applied for the first time. The model is validated and then employed to simulate the reactive transport in five different microreactors with dissimilar structural parameters. Comparison of the results shows that the fibers orientation is an effective geometric parameter that can greatly influence the hydrogen production efficiency.     

High-order iterative learning model predictive control for batch chemical processes

In order to address two-dimensional (2D) control issue for a class of batch chemical processes, we propose a novel high-order iterative learning model predictive control (HILMPC) method in this paper. A set of local state-space models are first constructed to represent the batch chemical processes by adopting the just-in-time learning (JITL) technique. Meanwhile, a pre-clustered strategy is used to lessen the computational burden of the modelling process and improve the modelling efficiency. Then, a two-stage 2D controller is designed to achieve integrated control by combining high-order iterative learning control (HILC) on the batch domain with model predictive control (MPC) on the time domain. The resulting HILMPC controller can not only guarantee the convergence of the system on the batch domain, but also guarantee the closed-loop stability of the system on the time domain. The convergence of the HILMPC method is ensured by rigorous analysis. Two examples are presented in the end to demonstrate that the developed method provides better control performance than its previous counterpart.     

Multi-dimensional design process management by extended product modeling for concurrent process engineering

Conventional product and process models have focused on static features. That means product models are mainly based on structural decomposition of products, and process models are also often described by activity decomposition such as work breakdown structure. From the view of design process management, it is difficult to describe dynamic features of design processes appropriately through conventional methodologies. In this paper, a multidimensional approach for design process management was explored to manifest characteristics of design processes for chemical plant design. Parallelized design process for concurrent process engineering should be managed by twodimensional design activity flows. The process management makes it possible to guide progress of design processes in a helix structure by horizontal and vertical activity control simultaneously. They stand for teleological and causal relation between design activities, respectively. That can be achieved based on an extended product model, which represents various design perspectives explicitly from a conventional design activity model. The extended product model is composed of product data, design activities, and activity drivers. Dynamic features of the extended product model are expressed by an activity chain model. These concepts will support the realization of concurrent process engineering for chemical plant design in the sense that they provide design process management strategies.