Search

Scientific advances in the fields of chemistry and engineering have established the critical impact of solvents on the rate of a wide array of chemical reactions. This has triggered the interest of academia and industry for the search of solvents that optimize reaction kinetics. Yet, there are only a few systematic approaches to guide such solvent selection currently. In this work, a methodology was developed to identify suitable solvents that will enhance the reaction rate of an industrial process at minimum experimental effort. For the rapid data generation and quantification, a modular continuous reactor coupled with real-time in situ analytics was set up. The data generation is guided by a computational method that consists of two steps: the formulation of a model identification problem based on the solvatochromic equation and the computer-aided screening of a solvent database that provides a list of the most promising solvents. The methodology is applied to the amination of ethyl trichloroacetate with liquefied ammonia, a reaction of industrial interest. Two of the predicted promising solvents are verified experimentally, demonstrating the predictive power of the methodology. Thus, a systematic model-based solvent selection methodology was demonstrated successfully to an industrially relevant reaction problem.

Oxygenated species obtained from the selective chemo-catalytic refunctionalization of lignocellulosic materials and rationally formulated mixtures thereof can be tailored to the needs of advanced internal combustion engine concepts like low temperature compression-ignition or highly boosted spark-ignition combustion. In the present contribution, we present a framework for model-based formulation of biofuel blends with tailored properties by considering the fuel’s molecular composition as the fundamental design degree of freedom. To this end, optimization-based reaction pathway screening is combined with mathematical fuel property prediction by means of structural group contribution and quantitative structure–property relationship modeling in order to formulate and solve an integrated product and pathway design problem. The model-based approach is envisioned (i) to guide fundamental experimental investigations of the combustion behavior of blended biofuels toward the most favorable mixtures and (ii) to identify promising conversion pathways for further elaboration by means of reaction engineering and conceptual process design. The latter is ultimately needed to bridge the gap from the mass- and energy-based molecular level analysis presented here to a process level analysis addressing the economics of the involved conversion and separation steps. A case study is dedicated to the identification of promising blends and respective production routes for the spark-ignition (SI) engine. The goal here is to produce a 100% bioderived fuel with tailored properties from hexoses and pentoses of lignocellulosic biomass by utilizing renewable hydrogen from carbon-free energy sources such as wind or solar to boost the energy of fuel produced for a given quantity of biomass supplied.

A novel type of iterative partition-based moving horizon estimators (PMHE) is proposed, the estimates of which approach those of a centralized moving horizon estimator as the number of iterations increases. It uses a deterministic setting and can handle known inputs as well as bounds on the estimated state. We derive conditions on the system, its partitions, and the scalar regularization parameter, which guarantee convergence towards the optimal centralized state estimate as well as stability of the estimation error dynamics, even with a finite number of iterations at each time step. Finally, numerical simulations demonstrate both the features and competitive estimation quality of the proposed method. [ABSTRACT FROM AUTHOR]

In this note, a solution method is presented for nonlinear model-predictive control of open-loop stable systems on an infinite horizon. The proposed method first reformulates the infinite-horizon continuous-time problem by a time-coordinate transformation as a finite horizon problem and computes the solution after discretization of the control variables. This method aims to ensure stability without imposing a terminal constraint set. The adaptive discretization algorithm allows an efficient and accurate solution of the infinite-horizon problem with a moderate number of discrete decision variables. The time transformation function is adapted such that the important dynamics of the system can be captured and the control variables can be discretized appropriately. An illustrative case study is presented. [ABSTRACT FROM PUBLISHER]

AbstractThis paper presents the OptoEcon-Toolbox, a modular software environment for the simulation of chemical and energy processes, and their control using optimizing sampled-data controllers. Key features of this toolbox are modularity, which allows the realization of general multi-layer control architectures, and interfaces to powerful numerical software, which allows the usage of controller and plant surrogate models formulated in different modeling languages such as Modelica or gPROMS. The capability of this toolbox is illustrated in a case study of an industrial copolymerization process.

For the first time, a distributed output feedback control scheme is presented which combines distributed model predictive control with distributed moving horizon estimation. More specifically, we combine the iterative methods of sensitivity-driven distributed model predictive control (S-DMPC) with sensitivity-driven partition-based moving horizon estimation (S-PMHE). To that end, S-PMHE is extended such that it can handle inputs of S-DMPC. The resulting distributed output feedback scheme is then applied to an alkylation benchmark process from the literature. We find that its control performance is comparable to that of fully centralized MPC and MHE but our distributed output feedback scheme is faster.

Recently, novel biofuel candidates have been proposed by catalysis research. To guide follow‐up research activities efficiently, it must be assessed whether large‐scale production of such next‐generation biofuels is competitive in comparison with established biofuels like ethanol. However, it is very time‐consuming to provide complete process designs for all alternatives, especially as important data are missing in the early development phase. Instead, reaction network flux analysis (RNFA) has been introduced recently for rapid screening of production routes to bridge the gap between catalysis research and process design. Using this approach, mass balances are formulated for a network of possible reactions such that multi‐objective optimization techniques can be applied to identify the attractive pathways with high performance. Typical performance indicators like total annualized cost or environmental impact are adapted to the requirements of the early design phase and integrated in the flux analysis. In this paper, production performance indicators of ten novel biofuel candidates are compared to those of methanol, ethanol, butanol, and methane as reference fuels using RNFA. While some pathways already show an encouraging performance, the reaction yield of others must be increased significantly, if the target product should qualify as promising fuel candidates. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd

We present algebraic conditions on the trajectory of a dynamical system to approximately describe a certain type of system robustness. The corresponding equations can be used as constraints in a robust optimization procedure to select a set of optimal design parameters for a dynamical system which is subject to fast disturbances. Robustness is ensured by requiring the disturbance parameters to stay sufficiently far away from critical manifolds in the disturbance parameter space, at which the system would lose stability. The closest distance to the critical manifolds is measured along their normal vectors. [ABSTRACT FROM AUTHOR]

A novel distributed model predictive control method for linear discrete-time systems is considered. The method can handle coupled constraints as well as coupled objective functions. For the decomposed optimal control problems embedded in the distributed controller additional linear information on the full optimal control problem is added. On each horizon, the resulting coordinated optimal control problem is solved iteratively. Prices and resources are calculated simultaneously. A convergence analysis is presented as well as a case study for an unstable process. There, an almost optimal control sequence is achieved after only one iteration.

We propose in this note a method for computing the solution to the infinite horizon continuous-time constrained linear quadratic regulator. The method is based on two main ingredients: a multigrid method for placing a finite number of time intervals, and a piece-wise linear parameterization of the input within the intervals. The input values at the decision-time points and slopes within the time intervals are computed via quadratic programs (QPs). The grids are gradually refined to efficiently improve the accuracy of the solution, and the required matrices and vectors for all QPs are computed offline and stored to improve the online efficiency. Two examples are presented to show the main characteristics of the proposed method. [ABSTRACT FROM PUBLISHER]

No Abstract

Eine nachhaltige Versorgungskette: Die kontrollierte Überführung der aus Biomasse erhaltenen Plattformverbindungen Lävulinsäure (LA) und Itaconsäure (IA) in die entsprechenden Lactone, Diole oder cyclischen Ether (siehe Bild) wird durch einen multifunktionellen molekularen Katalysator möglich.

In dem Beitrag wird ein Regelkonzept für die Beladung eines Schichtladespeichers zur Warmwasserversorgung vorgestellt. Die Beladung erfolgt über die dem Speicher vorgeschalteten Komponenten Wärmetauscher und Brenner des betrachteten Heizungssystems. Dem Reglerentwurf liegt ein dynamisches Modell zugrunde, das aus fünf partiellen Differentialgleichungen für Brenner und Wärmetauscher besteht. Für den modellbasierten Reglerentwurf wird ein modularisierter Ansatz verfolgt, bei dem Brenner und Wärmetauscher separat betrachtet werden. Für den Brenner wird auf Basis eines reduzierten, nichtlinearen Galerkin-Modells mit Hilfe der Ein-/Ausgangslinearisierung ein entsprechendes Regelgesetz hergeleitet. Die Trajektorienfolge für die Austrittstemperatur des Wärmetauschers wird durch eine Vorsteuerung mit unterlagerter PI-Regelung realisiert. Dabei wird die Vorsteuerung durch Inversion der transzendenten Übertragungsfunktion des linearen verteilt-parametrischen Modells des Wärmetauschers sowie mit Hilfe einer numerischen Laplace-Rücktransformation auf Basis des Weeks-Algorithmus berechnet.

In the past decade the use of focused beam reflectance measurements (FBRM) has been established as an on line and in situ particle characterization technique. However, a model is required to obtain full information from the FBRM signal and to compare the results to other measurement techniques. Different modeling approaches can be found in the literature. All of these assume a laser focus of zero extension, motionless particles and fully opaque particles. It is shown in this work that these assumptions do not hold even for ideally spherical particles. For large, opaque particles, the particle velocity and a depth dependent laser velocity have to be considered. For highly transparent particles, backscattering only occurs near the edges of a crystal. Consequently, two more refined modeling approaches – the velocity modeland the edge scattering model– based on Monte‐Carlo techniques are developed and verified in this work.

Dieser Beitrag gibt eine Übersicht über neue Entwicklungen der Echtzeitoptimierung transienter Prozesse. Die Entwicklungen basieren auf einer Zerlegung des Prozessführungsproblems auf zwei durch unterschiedliche Zeitskalen gekennzeichnete Ebenen, die den wirtschaftlichen und den regelungstechnischen Zielen entsprechen. Zwei modellgestützte Strategien wurden entwickelt, welche die beiden Ebenen enger integrieren und auch bei Unsicherheiten eine nahezu wirtschaftlich optimale Prozessführung ermöglichen. Die Anwendung auf simulierte industrielle Prozesse mit verschiedenen betrieblichen Szenarien zeigen, dass mit diesen Strategien erhebliche wirtschaftliche Vorteile erreicht werden können.

Design of distillation columns using mathematical programming has been a research topic for over a decade. However, a lack of robustness and high sensitivity toward the initial solution have prevented the widespread use. To overcome the numerical problems of the optimization algorithms, a combination of shortcut calculations with the rectification body method (RBM) and rigorous optimization is proposed. All potential splits can be generated and ranked based on the minimum energy demand calculated in the shortcut step. The least attractive split alternatives can be discarded to reduce the complexity of the rigorous model, while the results of the shortcut calculations for the promising alternatives can be used further for the initialization and bounding of the rigorous optimization. The rigorous optimization problems are then optimized as successive relaxed mixed-integer nonlinear programming (SR-MINLP) problems:? instead of a combinatorial expensive MINLP problem, a succession of NLP problems with decreasing bounds is solved. An integer solution is achieved in all case studies considered. The robustness of the algorithm is improved, and the calculation times are reduced compared to existing methods. The design methodology is reliable and yields good results. It is illustrated with several case studies involving azeotropic mixtures.

Recent research in the field of dynamic optimization is striving for increased robustness and efficiency of the numerical solution methods. In addition, the physical interpretation of the optimal solution, with respect to the control switching structure, has been gaining increased interest, because it provides valuable insight that is useful for applying the optimization results on the process. In a previous publication, we have addressed this issue by proposing an automatic switching structure detection method. This paper presents an extension of this idea by combining the structure detection approach with a control profile adaptation technique. The resulting adaptive structure detection approach enables a faster numerical solution, which is more likely to contain the correct switching structure. This further enhances the numerical solution of dynamic optimization problems. The concept is illustrated by means of a small example problem and a large-scale industrial case study.

The stabilization problem for multi-input nonlinear systems exhibiting bifurcation phenomena is considered. The objective is to stabilize a part of the equilibrium manifold, while preserving all system equilibria. The problem is addressed by means of bifurcation placement (also known as anti-control of bifurcations). Using eigenvalue assignment techniques a washout-filter-aided dynamic state feedback control law is designed that introduces a Hopf bifurcation at a certain equilibrium and achieves the objective described above. The proposed method is applied to a simple model of a continuous stirred tank reactor.

We describe how parametric spectral models for analytical applications can be generated by an automatic curve-fitting algorithm. The algorithm does not require initial choices of parameters or other human intervention, in contrast to established approaches that rely on deconvolution or derivative spectroscopy. This algorithm has been applied for quantitative analysis but can potentially be used in other applications that are based on parametric representations of peak-shaped models or could benefit from using such models, such as calibration transfer.

We present an indirect hard modeling (IHM) approach for the quantitative analysis of reactive multicomponent mixtures with intermolecular interaction. It can be used when it is not possible to obtain calibration data in the composition region of interest. The goal of this work, specifically, is to analyze reactive systems, although the validation of the method is done with nonreactive systems. Compared to conventional hard modeling, the new approach reduces the manual work required for modeling and renders unnecessary the assignment of bands in mixture spectra to individual components. It is based on parametric models of the pure component spectra that are made just flexible enough to fit the spectra of the unknown mixtures, and it only requires small calibration data sets that may lie in different regions of the composition space. The application to infrared (IR) and Raman spectra of multicomponent systems is discussed.

The challenge for real-time optimization-based control systems is to efficiently handle uncertainty. A novel approach for closed-loop optimization is presented that systematically combines a fast update strategy with rigorous optimization when necessary. A parametric sensitivity-based technique is used to calculate optimal first-order updates to a nominal reference solution. The technique does not assume that the active constraint set remains the same after changes in uncertain parameters. In closed-loop, the approach is very effective to handle uncertainty while requiring only a minimum number of full realtime optimizations reducing the on-line computational expense. The approach is illustrated by simulations of closed-loop real-time optimizations of a semibatch reactor described by a model with different kinds and ranges of parametric uncertainty. It is observed that the closed-loop updated solution is almost identical to the true optimal solution corresponding to uncertainty

In this paper we present a novel method for the numerical solution of dynamic optimization problems. After obtaining a first solution at a coarse resolution of the control profiles with a direct sequential approach, the structure of the control profiles is analyzed for possible switching times and arcs. Subsequently, the problem is reformulated and solved as a multi-stage problem, with each stage corresponding to a potential arc. Order and resolution of the control parameterization are adapted to the type of the particular arc. With a case study we show that accurate solutions with only few degrees of freedom can be obtained.

In this work an approach based on nonlinear dynamics is used for the integrated design and controller tuning of a CSTR. The approach enables integrated design optimization and robust tuning of a linearizing feedback controller. The controller setting found by the approach guarantees robust stability of the process over a large range of set point variations even in the presence of parameter uncertainty. The method enforces robust stability by introducing lower bounds on the parametric distance of the operating point to critical boundaries in the space of process and controller parameters. For stabilization of a large range of operations, a lower bound on the distance to a nontransversal Hopf bifurcation has to be considered in this particular case.

Feedback linearization is a nonlinear controller design strategy that results in an explicit formulation of the feedback control law. This method can result in excellent performance if an accurate dynamic process model is available. However, feedback linearization suffers from a lack of robustness if plant-model mismatch exists. The approach presented in this work analyzes the robustness properties of the closed-loop process with specific regard to the controller tuning parameter. Due to this, it is possible to tune the controller such that robustness over the entire operating region is guaranteed even under the assumption of certain types of model mismatch. This method is illustrated with an example and conclusions about its applicability to more general model and controller formulations are presented.

This paper proposes an incremental approach for the identification of complex reaction kinetics in chemical reactors. The reaction fluxes for the various species are first estimated on the basis of concentration measurements and balance equations. This task represents an ill-posed inverse problem requiring appropriate regularization. In a further step, the reaction rates are estimated without postulating a kinetic structure. Finally, the dependency of the reaction rates on concentrations, i.e. the kinetic laws, are constructed by means of feedforward neural networks. This incremental approach is shown to be both efficient and flexible for utilizing the available process knowledge. The methodology is illustrated on the industrially-relevant acetoacetylation of pyrrole with diketene.

The estimation of reaction rates is an important problem in mechanistic modeling, monitoring and control of chemical reactors. In contrast to standard estimation techniques where a model must be chosen for the reaction rates, we consider them in this work as unknown time-varying functions, which also may be interpreted as inputs. The resulting estimation task is an ill-posed inverse problem. The paper addresses this estimation problem based on systematic methods for nonlinear system inversion and filtering resulting in efficient estimators. A theoretical analysis reveals the conditions for reaction rate reconstruction are those for system invertibility. Our estimation scheme is a regularization method which eliminates the difficulties arising with ill-posed problems. Guidelines for the design of the estimator structure and the selection of the regularization parameters are presented.

An approach for the optimal configuration and sequencing of complex batch‐distillation processes is presented. The proposed method is based on formulating a mixed‐logic dynamic optimization problem in which a performance index is minimized subject to a disjunctive dynamic model and constraints. The disjunctive model comprises a given set of equipment modules, such as stacks of trays of different size or feed and product vessels, available for the distillation process under consideration along with logical relationships provided by the design context. The optimization based on this model may yield unconventional design and operational strategies, and thus improve the economic efficiency to a significant extent. The logic‐based solution technique proposed in this work offers some favorable properties when compared to established mixed‐integer dynamic optimization problem solution techniques reported in the literature. The design of a multistage batch‐distillation process serves as an example to illustrate the proposed modeling and solution method.

A stepwise refinerment approach for sparse grids is proposed to recover an unknown function from a set of corrupted data. The work is based on the approach of Garcke et al. (2001), which is extended to cover non-unifonn discretizations in each dimension. Starting frorn an initially coarse discretization, the multivariate grid is gradually refined using sensitivity analysis. An appropriate level of refinelnent is identified by the L-curvecriterion or split-sample validation, capable to resolve functional details while suppressing measurement noise. It is shown that the algorithm allows reliable identification of the best possible functional approximation.

In this work model identification of state-dependent transport coefficients in distributed systems is considered. In many cases it is difficult to formulate a suitable candidate model based on physical insight or prior knowledge. General parameterizations have therefore to be employed leading to a large number of unknown parameters. This may be prohibitive for distributed systems due to the computational cost of parameter estimation. An incremental model identification procedure is therefore employed here. This approach reflects the model development process itself and splits the identification into a sequence of inverse problems. Thereby, uncertainty in each step is minimized and computational cost is reduced substantially. The implementation presented here uses results from inverse problems theory and is applied to the estimation of a concentration dependent diffusion coefficient.

The measurement of diffusion coefficients in multicomponent liquid mixtures is still an open question. A new technique aimed at overcoming this deficiency is presented. It is based on Raman spectroscopy, which has been extended to obtain liquid concentration data of all components in a mixture simultaneously with high temporal and spatial resolution. The following data analysis uses model‐based methods. This provides a very flexible framework, allowing substantial reduction of experimental effort and time and the direct estimation of the diffusion coefficient as a function of concentration. Furthermore, the experimental procedure is improved using optimal experimental design techniques. This new methodology is validated for binary mixtures.

Large changes in emissions also cause a significant change in pollutant concentrations in rain water. The influence of these changes on pollutant concentrations in rain water and wet deposition were investigated in different regions and time periods from 1983 to 1999 in East Germany. Initially, this period is characterized by large emissions of SO2(about 5400 kt a−1), NOx(about 750 kt a−1), and dust (about 2000 kt a−1) at the end of the 1980s. After the reunification of Germany in 1990 and restructuring of industry and agriculture, emissions drastically decreased. For example, from 1990 to 1998 in Saxony emissions of SO2, NOxand dust decreased by 84, 44 and 97%, respectively. Alkaline components also strongly decreased through efficient dust removal, while no desulphurization was used in flue gases of power and heating plants. As a consequence, the mean acidity of precipitation strongly rose by a factor of three from before 1990 up to 1995 (the mean pH value in 1995 was about 3.9, with minimum values down to 3.6). In 1996 desulphurization techniques were established in power plants and resulted in an increase of pH values to the level in the period from 1983 to 1989/1990. The results for ionic composition (Cl−, NO3−, SO42−, Na+, NH4+, K+, Ca2+, and Mg2+, the pH value (acidity), and conductivity) are based on precipitation samples collected in periods 4 h. The data were classified with backward trajectories and entry sectors which are characterized by similar emissions and/or geographical regions.

It is well-known that the performance of chemical reaction processes can be improved by periodic forcing. The π-criterion has been introduced and applied extensively to quantitatively assess the potential of periodic forcing in a particular case, though it is difficult to understand the physical reasons for the performance improvement. A theoretical analysis in the frequency domain and a study of typical model reaction systems in stirred tanks including parallel, consecutive and reversible reactions is presented here. It is shown that the performance improvement can clearly be related to resonance phenomena displayed by the linearized dynamics in conjunction with a nonlinear variation of the process gain in the region of forcing. The theoretical findings are illustrated by a number of examples.

The slow dynamic behavior of mass transfer in pervaporation has been reported by different authors. Until now, however, only a little experimental data and no model capable to describe this phenomenon have been available. A new detailed model for mass transfer in pervaporation, based on the dual‐sorption theory, Maxwell‐Stefan diffusion, and the UNIQUAC model, is proposed. This model is capable of properly describing mass transfer at steady state and during transients. Model parameters for two different PVA/PAN membranes for ethanol/water separation are given. The model shows good agreement with both steady state and dynamic experimental data.

This paper discusses changes in the market of chemical processing industries and its consequences for process operation. The problem of continuously declining capital productivity is tackled, which has been faced by the chemical processing industries during the past decades. In the paper a motivation is given on how new technologies in the area of model predictive control and dynamic process optimization can contribute to improve business performance of the chemical processing industries in the changed markets. These new technologies have to support dynamic operation of plants at intended dynamics. Extensions and changes required to technologies that are currently applied in the oil refining industries and in some petrochemical industry applications are discussed. These extensions and changes are necessary to meet the specific requirements of the chemical processing industries.

In recent years numerous approaches have been presented to support the activities in chemical process design by means of software tools, but significant improvements of the design process can only be achieved if it is supported in its entirety. Therefore, the integration of the existing software tools is of prime importance. All these tools are based on data models that capture the information handled during the design process. In this study, a conceptual data model is presented that has been designed for the integration of existing data models as a first step towards the integration of the software tools using these models. This conceptual lifecycle process model CLiP is based on the ideas of general systems theory. This universal approach renders the model very flexible and guarantees a well-defined structure for data model integration. Some integration mechanisms are presented and discussed. Finally, a conceptual model is proposed based on the architecture tool integration.

Because of the high dimensionality and the high degree of coupling the simulation of multicomponent mixture processes requires a suitable technique for model size reduction to facilitate the numerical solution if the mixture comprises a large number of components. Established techniques of pseudo-component lumping group (lump) the chemical species by introducing procedure can neither be estimated for controlled. A continuous compositional representation of the mixture introduces more flexibility in the model size reduction. Using a Galerkin discretization scheme in combination with a wavelet-based error control and adaptation strategy a model size reduction techniques is introduced which allows the specification of a desired accuracy. A simple example representing the thermal cracking of a hydrocarbon mixture modeled by means of the breakage equation is used to illustrate the new method.

In an earlier article of the authors, the rectification body method was introduced for rapidly calculating the minimum reflux of nonideal and azeotropic multicomponent separations. Here, the RBM is generalized to two important classes of complex columns: (1) columns having sidestreams, and (2) columns with one or several side‐enricher and/or side‐stripper columns. As in the case of simple columns, the method is entirely general and does not require any a priori information on the limiting pinches prevailing in the various column sections.

In this paper the results of a bifurcation analysis on heterogeneous azeotropic distillation sequences are given. Two sequences suitable for ethanol dehydration are compared: The ‘direct’ and the ‘indirect’ sequence. It is shown, that the two sequences, despite their similarities, exhibit very different static behavior. The method of Petlyuk and Avet’yan (1971), Bekiariset al. (1993), which assumes infinite reflux and infinite number of stages, is extended to and applied on heterogeneous azeotropic distillation sequences. The predictions are substantiated through simulations. The static sequence performances are compared for minimal impurities in both products, where the direct sequence exhibits output multiplicity, while the indirect sequence exhibits state multiplicity. The latter multiplicity may be avoided by accepting a slightly increased impurity in the ethanol product.

Observers are proposed as a solution algorithm for the inverse heat conduction problem (IHCP) of reconstructing a time-dependent surface heat flux at the boundary of a linear heat conductor. The derivation of optimal observer equations follows directly from a novel interpretation of the IHCP in the frequency domain: Solving the IHCP is viewed as a filter design problem in which the reconstructed heat flux is obtained by low-pass filtering of the true heat flux. It is demonstrated that observers are easier to tune, yield a better trade-off between the deterministic bias and the sensitivity to measurement errors, and lead to smaller-size computational problems than other solution approaches.

Zusammenfassung:

Zusammenfassung:

No abstract

No abstract