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RWTH-Themen : Berichte aus der Rheinisch-Westfälischen Technischen Hochschule Aachen 2009(1), 56-61(2009). special issue: "Wasser und Leben / [hrsg. im Auftr. des Rektors: Dezernat Presse und Öffentlichkeitsarbeit der RWTH Aachen. Verantw.: Toni Wimmer]", Published by RWTH, Aachen

The transition towards a climate-neutral economy pushes the one-hundred-year-old ammonia industry to search for sustainable alternatives that reduce the fossil fuel dependency. At the same time, the growing penetration of variable renewable resources amid the Energiewende, driven by strong cost reductions and policy support, is becoming more and more system-relevant and asks for flexibility solutions on the utility scale. Power-to-Ammonia (PtA) technologies provide means to tackle the challenges of the individual sectors jointly. In this dissertation, two enabling process concepts and their potentials are systematically investigated. At first, a highly efficient ammonia-based concept is developed for large-scale energystorage. It utilizes a reversible solid oxide fuel cell for power conversion, coupled with external ammonia synthesis and decomposition processes and a steam power cycle. A refrigeration cycle is utilized to recycle nitrogen and oxygen in alternating mode and hence enables the closed-loop operation of the energy storage. Through heat integration and first-principle model-based optimization, the new energy storage concept demonstrates an electrical round-trip efficiency above 60%. Its levelized cost of storage can be obtained at the same level to that of pumped hydro and compressed air energy storage. Next, a renewable ammonia production process that is suitable for conducting demand side management (DSM) is proposed. A vertical integration of nitric acid production facilitates efficient heat integration between steam electrolysis and the rest of the process. The economic performance of the production complex is investigated through a model-based dynamic optimization approach, considering scenarios with or without incorporation of intermittent wind power as well as deployment of battery storage. In all cases, the wind power integration proves to be economic with a peak-to-base load ratio of up to 2.3. The new process reduces primary energy consumption by more than 13% compared to conventional technologies.Finally, the potential of implementing the PtA process concepts in the German energysystem is analyzed. To identify contributions of individual technologies, a consistent system optimization approach is carried out in a series of scenarios that build upon each other. Compared to a scenario without any PtA technology, the deployment of the proposed ammonia-based energy storage system in the national grid proves essential to limit renewable electricity curtailment and to completely eliminate CO2 emissions in power generation. Adding flexible production of renewable ammonia to the grid helps reduce the need for grid storage. Through actively exploiting power price signals, the CO2 abatement cost for renewable production is lowered by up to a half to below 150€/t CO2. The revenue from offering DSM is also identified to be the key for the domestic ammonia industry in competition with renewable production abroad with abundant renewable resources. Beside chemical product, the value of importing renewable ammonia for energy end-products is investigated. While it is not an economic hydrogen-carrier, ammonia could be more attractive than hydrogen used for power delivery over long distances.

Electrodialysis is an efficient process for seawater desalination that involves various interacting phenomena. In this process, ions are transported by flow, diffusion and an electric force and separated by selective membranes. For the optimization of this process, it is important to understand these interactions. This work presents rigorous mathematical models to describe the overall process and develops a numerical strategy for its simulation. With this approach it becomes possible to simulate the involved physical effects and their interactions in detail. To achieve this, the Maxwell-Stefan equations for mixtures are used. They take into account the electrical force and the multicomponent interactions with concentration dependent diffusivity coefficients and thermodynamic factors. Additionally, the usual assumption of local electroneutrality is not assumed to allow the nonideal effects in the electrical double layer near the membrane. For the numerical solution of these equations, the multicomponent lattice Boltzmann method (LBM) is developed and implemented in the solver Musubi. This model for the channel flow is coupled with an electric field and a model for the membranes. To obtain the electric field, the LBM that solves the Poisson’s equation is implemented in Musubi. The channels between the membranes are realized by spacers with complex geometry. A mesh generator (Seeder) on the basis of octrees is developed to ensure the appropriate discretization of the mesh for these channels. An essential part of this work is dedicated to the development of the parallel scaling coupling tool APESmate. APESmate is developed within the APES suite along with Seeder and Musubi on a central octree data structure that allows efficient handing of I/O on large scale distributed parallel computing systems.The developed software is used to compare the nonideal multicomponent model for various concentrations and surface potentials. The results show that nonideal effects increase with the concentration, especially in the electrical double layer. The spacers for various hydrodynamic angles and inflow velocities near and away from a sealed corner are investigated to find the design with reduced pressure drop and without low velocity zones. The highly resolved simulations show that the pressure drop increases with the hydrodynamic angle, while the extend of the low flow regions decreases.

Membrane distillation (MD) is an emerging technology for seawater desalination. The main objectives of this research study were: (1) to design, fabricate, and install a pilot scale direct contact membrane distillation (DCMD) testing facility, (2) to carry out experimental investigations on the performance of the DCMD pilot scale facility in terms of distillate production rate, recovery ratio, and performance ratio, and identify the most important and optimum operating conditions using orthogonal experimental design approach to carry out experimental sensitivity analysis, (3) to carry out short- and long-term experimental investigations at transient conditions, (4) to develop a 2D spatio-temporal model of DCMD that consists of experimentally-validated parameters, and (5) to validate the 2D dynamic model at different operating conditions. Orthogonal experimental design, correlation analysis and response surface charts were used to identify the parameters influencing the operational efficiency of DCMD. The orthogonal array design method was used to optimize the number of experimental trials required for dependence analysis. The operating conditions studied were feed inlet properties (temperature, salinity, flowrate) and distillate inlet properties (temperature and flowrate). The impact of those operating conditions on three DCMD performance indicators - distillate production rate, performance ratio and recovery ratio – were investigated. and confirmed by using the Pearson product-moment correlation coefficients. The major foulants on the membrane surface were identified through membrane characterization. The characterization methods employed include scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy, energy-dispersive X-ray spectroscopy (EDAX), streaming potential analysis, contact angle measurement, and membrane pore analysis. 2D dynamic model was developed from convective and diffusive heat and mass transfer to predict water flux across the membrane, temperature polarization, concentration polarization, and response of water flux to operational step changes. Aside the membrane module, the dynamic profiles of mass and temperature in the system peripheries, i.e. brine and distillate circulation tanks, were also modelled. Model parameters (kf, kp, αHeatloss, αCoil) were identified for different operating conditions using raw seawater and analytical grade NaCl solution; model accuracy was evaluated by comparing the model predictions before and after parameter estimation; and the calibrated model was compared with existing literature models to test model robustness. Higher accuracy of predictions for water flux across the membrane, relative to literature and experimental data, was achieved using the identified model. The 2D dynamic model was able to predict (i) 2D dynamic profiles of the temperature and concentration of the feed across the feed channel, (ii) 2D dynamic profile of temperature across the permeate channel, (iii) dynamic profiles of temperature and concentration polarization in the module and how the polarizations change along the flow direction, (iv) the dynamic profiles of temperature, concentration and mass in the peripheries, and (v) the dynamic profile of water flux in the module and how the profile changes along the flow direction.

Dissertation, RWTH Aachen University, 2018; Aachen 1 Online-Ressource (XIII, 146 Seiten) : Illustrationen, Diagramme (2018). doi:10.18154/RWTH-2018-223767 = Dissertation, RWTH Aachen University, 2018, Scientific advances in the field of chemistry have established that solvents have a critical impact 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 solvent selection in this direction and to date, a systematic model-based approach that considers direct applicability to industrial reaction problems has not been reported. In this study, a hybrid experimental/model-based solvent selection methodology built around the solvatochromic equation is established for the prediction of the best solvent(s) for an industrial chemical process at minimum experimental effort. For the rapid data generation and quantification, a modular continuous reactor coupled with real-time analytics has been set up. The solvatochromic equation was used to model the solvent effects on the reaction rates. The solvents that were experimentally investigated were selected from a solvent database consisting of known organic solvents and structures generated with the aid of CAMD techniques which was further refined to meet the specific reaction requirements, environmental and health constraints and equipment limitations. The selection was diversity-oriented, aiming at the acquisition of the maximum possible information at the least experimental effort. The methodology was applied on the amination of ethyl trichloroacetate with liquefied ammonia, a reaction of industrial interest. Two of the predicted promising solvents were verified experimentally, demonstrating the predictive ability of the methodology. The established methodology can be used as a starting point for further improvement. Inclusion of ionic liquids, supercritical fluids and structures generated with the aid of Computer Aided Molecular Design (CAMD) techniques in the solvent database, may reveal new promising solvent candidates, opening new windows in chemical synthesis. Coupling with model identification and discrimination techniques can further minimize the experimental effort involved. The current approach deals with the enhancement of the rate of the reaction leading to the desired products and the reduction of the rate of the side reactions as two independent objectives. Future research could be dedicated into treating the two objectives as a unified one., Published by Aachen

This thesis proposes a set of novel partition-based moving-horizon state estimation schemes for systems that are composed of interconnected and potentially geographically distributed subsystems. The state variables of every subsystem are viewed as partitions of the full state vector, and they are estimated in parallel by dedicated subsystem state estimators that can exchange information among each other. Not only can this approach be faster than a centralized moving-horizon estimator, but it also avoids the unfavorable dependence on a single central computer. The novelty of the proposed methods is their iterative nature. As a result, their estimation accuracy approximates the optimal estimation accuracy of centralized moving-horizon estimators arbitrarily well. Depending on which of the proposed methods is employed, the state of linear or nonlinear systems can be estimated. Moreover, different types of process and measurement uncertainties as well as additional inequality constraints of varying complexity can be taken into account. Theoretical results are developed and proven, which provide conditions for the convergence of the iterations at every sampling instant and for the stability of the estimation error as time proceeds. In particular, one of the proposed methods has the unique feature of simultaneous convergence and stability for a certain class of linear systems, independent of their subsystem topology. These theoretical results are validated by extensive numerical simulations, which also provide additional insights into the role of the different parameters and problem formulations on the dynamical behaviour of the proposed estimators. Further simulations illustrate the potential of the novel iterative partition-based moving-horizon estimators for industrial applications. First, the dynamic state estimation problem of large-scale power system networks is addressed. Assuming ideal parallelization, the proposed partition-based estimator is found to be nearly as accurate but faster than a centralized moving-horizon estimator. Secondly, by combining one of the proposed methods with a distributed model predictive controller from the literature, a completely distributed and optimization-based output feedback solution is developed and successfully applied to a chemical plant. Finally, a summary of the results and suggestions for promising future research directions conclude the thesis.

Typical continuous process flowsheets include reaction section, separation section and recycles. The reaction section is often the most important part of a chemical process, which may contain several interconnected reactors. The superstructure approach is a widely used model-based process design method for reactor network synthesis. It starts from a reactor network superstructure and uses mathematical models and optimization tools to select the best process design. The superstructure approach results in an optimal process flowsheet with determined connection patterns of reactors, reactor types, design parameters and operating conditions of each reactor. In this work, a systematic model-based approach for reactor network synthesis problems with guaranteed robust dynamic performance will be presented. The work is based on the superstructure approach, but in comparison to the classical methods, not only economic optimality with respect to a static objective function, but also certain specified dynamic properties, i.e. dynamic stability and response speed, are guaranteed simultaneously under parametric uncertainty. Structural alternatives in the flowsheet, i.e., how reactors are interconnected, as well as in the control system, i.e., how controlled and manipulated variables are paired, are subject to design degrees of freedom. Moreover, it is allowed that idle reactors and controllers can appear in the reactor network superstructure, so that a fixed number of non-idle reactors and controllers does not have to be assumed as a priori. The optimal reactor network design in either open- or closed-loop is determined by solving a single optimization problem. The proposed approach allows an integrated treatment of parametric uncertainties, which may either result from model uncertainties, such as reaction kinetic constants or heat transfer coefficients, or from process uncertainties, including slow disturbances in load or the quality of raw materials. A robust eigenvalue constraint to guarantee the robust performance of the designed reactor network is formulated. Efficient formulations of interconnecting reactors and novel complementarity-based constraints for control structure selection are proposed. The method results in a semi-infinite mixed-integer nonlinear optimization problem with complementarity constraints, disjunctions and a robust eigenvalue constraint. A hybrid two-step solution method is proposed to solve the synthesis problem, which integrates candidate solution algorithms of related optimization problems. The proposed solution method is applied to a case study of allyl chloride production with up to ten plug flow and continuous stirred tank reactors.

This thesis addresses important challenges that have to be overcome to facilitate a wider application of economic nonlinear model-predictive control in industry. One of the reasons justifying its application in place of other control methods is that the economic control performance may be significantly improved. Hence, the main focus of this thesis lies on achieving the best possible economic control performance at any time. To achieve this, a comprehensive formulation of the economic optimal control problem integrating the control task, the optimization task and the scheduling task as well as a specifically tailored numerical solution strategy are presented. Furthermore, a suitable model-predictive control class is chosen. After addressing feasibility and stability issues, existing fast nonlinear model-predictive control schemes are reviewed. These schemes substantially reduce computational delay such that real-time applicability becomes possible and performance losses are diminished. Supported by the results of the review, a fast economic nonlinear model-predictive control scheme is proposed which also accounts for all aspects ensuring the best possible control performance at any time.

This thesis addresses the reduction of differential-algebraic equation (DAE) systems. Modelling of physical or chemical processes often results in an equation system with both differential and algebraic equations. For various applications (e.g. applications in real-time) the simulation time of the model should be sufficiently short, which might not be the case when the model size is too large. In order to reduce the simulation time, mathematical methods for model reduction are deployed. In this thesis, a new method based on proper orthogonal decomposition (POD) is proposed for the reduction of linear and nonlinear DAE systems which performs better than the POD methods available in literature. The proposed POD method involves a system transformation which separates the DAE system into a dynamic subsystem and an algebraic subsystem before reducing the system by a Galerkin Projection. The connection between the POD reduction method and the balanced truncation reduction method which exists for ODE systems is generalized to DAE systems in this way. Linear DAE systems with an arbitrary differential index can be reduced by the proposed method in contrast to the POD methods previously proposed in literature that might fail for systems with a differential index larger than one. The various empirical reduction methods are also applied to a nonlinear DAE system with strangeness index zero. The newly proposed method is able to reduce this type of system, where one of the classical methods fails. Since reducing the size of a nonlinear system by projection does not always result in a shorter simulation time, a grey-box modelling approach is proposed, which replaces the high number of computationally expensive nonlinear functions by a smaller number of parametrized nonlinear functions.

The selection of proper solvents can be a key decision variable in engineering process attributes towards a certain objective. This contribution outlines a systematic framework for screening and/or design of solvents under both model parametric and structural uncertainty. The framework integrates model-based and data-driven methods. The model used is based on molecular descriptors, such as Kamlet-Taft parameters. The contribution addresses the selection of not only traditional organic solvents but also sophisticated novel solvents for chemical reaction engineering and biomass processing.This contribution demonstrates that the proposed framework is able to identify promising reaction solvents for a class of SN1 reactions amidst uncertainty in the data. The undesirable uncertainty propagation is treated using a combination of Tikhonov regularization and optimal design of experiments. The uncertainty propagation analysis employing Monte Carlo simulations demonstrates the advantages of employing the proposed framework over another method based on chemical insights. This contribution discusses the application of the framework on cellulose dissolution in ionic liquids which quantitatively reveals the contribution of each Kamlet-Taft parameters in this context. Hydrogen-bond acceptor basicity is dominant but not the sole contributor. By combining Kamlet-Taft parameters and some specific molecular structures, two separate regions of cellulose dissolving and non-cellulose dissolving ionic liquids can be characterized. This contribution discusses the application of the framework on the selection of the liquid solvent to be paired with the compressed CO2 and its composition in gas-expanded liquids for a Diels-Alder reaction. A mixed-integer nonlinear optimization model which incorporates Bayesian multimodel inference is proposed. Two reformulation strategies, tailored big-M and binary multiplication, are proposed in order to achieve better computational performance. Three CNIBS/R-K models are shown to be inferior to two preferential solvation models in predicting the Kamlet-Taft parameters of CO2-expanded liquids.

The optimization of membrane bioreactor (MBR) operation by nonlinear model-predictive control (NMPC) with activated sludge models is investigated in this work. To this end, different variants of NMPC are applied to simulation models of single-train MBR systems in order to demonstrate the technical feasibility of this concept and assess the economic impact of NMPC to full-scale systems.The application of economic NMPC to the model of a single-train MBR system under nominal conditions shows that the electricity cost of single-train MBR systems can be reduced by up to 7-10% and carbon dosage costs by up to 15%, compared to well-tuned conventional control. The economic potential of NMPC is shown to be greatest for control applications with tight effluent limits and a large number of control actuators.The feasibility of economic and ecological NMPC for full-scale MBR is tested by simulation with realistic measurement feedback from on-line measurement. Simultaneous optimization of economic and ecological plant performance is realized by a hybrid discrete-continuous NMPC algorithm which considers economic and ecological control objectives. The algorithm calculates optimal switching times between the objectives together with the optimal control inputs, satisfying optimality conditions defined for the overall control problem. It is shown that the chosen NMPC approach performs robustly when combined with a well-tuned Extended Kalman Filter and a fast trajectory-tracking NMPC despite imperfect state estimator performance and inaccurate disturbance predictions.Directions for future research are pointed out in a discussion of the process control challenges for large-scale MBR and the challenges which need to be addressed to implement this technology in industrial practice.

The future chemical utilization of biomass as a renewable raw material requires a pretreatment to gain access to these macromolecules. This work describes the systematic development of such a pretreatment process using ionic liquids to convert wood into fermentable sugars. During the exploration of the effects of several ionic liquids on wood, a disintegration effect is discovered in this work, which is identified as the most promising pretreatment mechanism. Its benefit is demonstrated by a high yield of 65% of sugars after a short enzymatic hydrolysis. Moreover, the experimental results are exploited in a process simulation to assess the energy demand and the economic prospect of the developed pretreatment concept. This analysis identifies advantages and bottlenecks of the biomass pretreatment with concentrated solvents such as ionic liquids. A discussion outlines the challenges to be tackled by further research to tailor pretreatment processes to biomass and economy.

Hybrid separation processes offer a great potential for the design of energy-efficient, sustainable separation processes through a combination of different separation techniques. However, the design of these highly integrated processes is challenging due to the multitude of structural and operative degrees of freedom. A lack of modeling experience and reliable synthesis methods has so far hindered the application of these promising designs in industry. It is the scope of this thesis to provide methodologies which facilitate an efficient and reliable conceptual design of hybrid separation processes. For this purpose, a synthesis framework for the optimization-based design of hybrid processes is proposed. Powerful shortcut and rigorous evaluation methods for distillation, heteroazeotropic distillation, extraction, crystallization and reactive distillation are developed. These methods are fully algorithmic and computationally efficient in order to allow an optimization-based design of large-scale hybrid processes. The proposed synthesis framework is validated by large-scale industrial case studies.

The objective of the presented thesis is the development of a software-technical and algorithmic solution for the dynamic simulation of complex multi-scale problems in the field of crystallization and fluid dynamic process modeling. In the thesis, all aspects of the problem solution are considered. The proposed solution is based on the representation of the complex problem in the form of a generalized process flowsheet. This flowsheet is solved by the specialized software simulation tools coupled by means of an integration platform CHEOPS. CHEOPS supports representation of the process flowsheet and includes the algorithms for the flowsheet simulation. The units of the flowsheet (usually, the apparatuses) are represented by externally stored mathematical models and solved by the simulation software. To enable integration into the flowsheet model, interfaces to a number of external software tools such as FLUENT, Parsival, gPROMS, MATLAB and HYSYS have been implemented in CHEOPS. The modular dynamic simulation algorithm for the solution of the flowsheet problem was developed and tested first on the illustrative example, which represents a crystallization process flowsheet. The developed coupled simulation approach is further applied to the solution of the multi-scale problem, which involves a fluid dynamics subproblem and crystallization subproblem described with the population balance and the crystallization kinetics. This multi-scale problem is represented as a generalized flowsheet, where process phenomena are represented as flowsheet units. Different decomposition options and choices of the coupling variables to be transferred between the subproblems are analyzed. As the considered phenomena have different scales, discretization grids for the individual subproblems have to be chosen. The problem decomposition is performed such that for each subproblem, the best matching spatial grid is determined. The fine spatial grid is introduced for the fluid dynamics, and the coarse grid (compartments) is introduced for the crystallization subproblem. Scale integration techniques to bridge between the grids are implemented and evaluated. The error sources in the coupled simulation are discussed and the problems that arise in the error estimation are formulated. The method was successfully applied to an illustrative example, for which the validation using a reduced approach (Method of Moments) was possible, and the errors can be evaluated. It was found that the two major causes of deviation from the reference solution are inconsistencies in the problem formulation between the subproblems, which cannot always be avoided, and the choice of the coarse grid, which introduces discretization error for the quantities within the compartments. Further development of the method was done by introducing a compartment adaptation, where the compartment boundaries are adjusted according to specified criteria during runtime using the adaptation procedure developed in the study. Simulations with adaptation were performed for different choices of criteria. The adaptation method showed ambiguous results depending on the choice of criteria. In particular, the predictions improved when both the kinetics and the residence times were accounted for. The developed generalized flowsheet method was applied for the complex case study where the crystallization and the fluid dynamics models were solved for a lab-scale crystallizer and state-of-the-art models of the process kinetics, taken from the literature. The method succeeded to simulate this model as a generalized flowsheet and can be used for the other problems with similar complexity. However, due to large differences of time scales of the subproblems, the simulation time was large, thus the model solution was found to be dependent on small disturbances, and the simulation accuracy was insufficient.

Water is a global resource indispensable for life on earth. Its responsible and sustainable use and reuse is a major challenge of the 21st century. The increasing world population and industrialization lead to a rising demand for potable and process water, and in many areas existing supplies are diminishing at critical rates. Untreated wastewater threatens intact biological systems by introducing large amounts of nutrients, toxic or endocrinous species, heavy metals, and other harmful components. For these reasons efficient water treatment and reuse have become decisive social and economical issues. In many countries legal limits on the effluent concentrations of selected components are tightened, e.g. by the European Water Framework Directive issued in 2000. Strict effluent constraints however together with increasing wastewater loads demand efficient treatment processes. At the same time the increasing privatization of wastewater treatment facilities requires a stronger focus on their economic performance. This context provides the motivation for the research presented here. The technology of interest are membrane bioreactors (MBR) for wastewater treatment, which have increasingly been employed for the last 15 years and which are expected to play an important role in future wastewater treatment. MBR combine classical biological wastewater treatment with subsequent membrane filtration. The membrane unit separates the biomass of the biological treatment from the water. MBR offer high effluent quality, reliable biomass separation, and small space requirements. These properties make them an appealing alternative especially when effluent constraints are tight, when space is limited, and when existing plants need to be upgraded. In general, however, MBR operating cost are higher than those of conventional wastewater treatment plants (WWTP), which employ sedimentation basins for the biomass separation. A large potential to increase the economic feasibility of MBR lies in the improvement of their operational policy. Until today only simple control strategies have been employed. Advanced control approaches frequently used in the chemical process industry have not been applied to MBR due to the large uncertainty in the biological and the filtration processes, in the inflow prediction, and in the limited measurement information. While this is not different from the obstacles in regular WWTP operation, the increased complexity of MBR even more requires efficient online control to exploit their full potential. Hence, this thesis focuses on the process control of MBR. It aims at bringing advanced approaches from many research areas as e.g. modeling, control, and optimization together to provide a capable, flexible, and generic control architecture which takes the characteristics and peculiarities of MBR and MBR operation into account. Due to the process complexity model-based control approaches are proposed. Time and unit scale separation are performed to obtain subproblems of lower complexity for different disturbance dynamics and for both the biology and the membrane system. The subproblems include the scheduling of operational strategies, dynamic real-time optimization, non-linear model predictive control, run-to-run control, inflow prediction, and state and parameter estimation. For each of them suitable models, problem formulations, and efficient solution algorithms need to be formulated. The coordination between the subproblems on different time scales and between the units must be considered. Available solutions are discussed and complemented by new models and algorithms. The framework provides a clear input-output structure in order to enable researchers to easily incorporate extensions and modifications. Due to its modular design, the solutions developed can be applied to WWTP and filtration systems as well as to the MBR process.

Aachen, Techn. Hochsch., Habil.-Schr., 2008; Aachen : Publikationsserver der RWTH Aachen University XVI, 200 S. : graph. Darst. (2008). = Aachen, Techn. Hochsch., Habil.-Schr., 2008, This book presents and assesses modeling concepts for predictive crystallization modeling. The focus is on the influence of a proper representation of the crystal geometry and its implications on modeling methodologies. The shape of crystals is not only relevant to the properties of the final product but also affects process behavior. The model-based investigation of shape-dependent behavior has been hindered due to lack of computational power and deficiencies of available modeling concepts. It is shown that the complex characterization of particles and its use in process modeling is an emerging field with a large potential for fundamental improvements in model predictiveness., Published by Publikationsserver der RWTH Aachen University, Aachen

Waste water treatment plants with submerged membrane modules form an interesting alternative to common waste water plants where the separation of particulate substances from the purified water is achieved by large clarifiers. The advantages of the membrane bio reactor comprise better waste degradation resulting from higher biomass concentration, improved effluent quality even under strong feed fluctuations and a more compact plant layout due to the absence of the clarifier. However, operational costs of this type of plant currently exceed the operational costs of common waste water treatment plants. Therefore, the presented work formulates a model based on-line optimization scheme in order to guarantee effluent quality under varying feed scenarios while simultaneously minimizing the operational costs. Due to the varying amount and concentration of waste in the inflow, the complex biological transformation steps and necessary simplifications a model based optimization approach requires the combined estimation and reconstruction of the model states and disturbances and also relies on a good predictive model describing the main phenomenon in the waste water treatment plant. This work presents a decentralized optimization based control framework that exploits different time scale and results in two separate optimization problems for the biological waste degradation and the operation of the membrane modules. The objective for the biological part is to guaranty effluent constraints for certain species, while minimizing the operational costs. However, under storm water scenarios it may be necessary to change the operational strategy in order to cope with the amount of waste water that has to be processed. Under this conditions the control strategy has to guarantee the constraints on the effluent concentration and the throughput through the plant withoutreaching a state where off-spec waste leaves the plant. This situation is possible if several optimization constraints act simultaneously and thus reduce the flexibility of the plant. Therefore, in the presented work the control strategy is selected on-line based on the current state of the plant and on a prediction of the plant behavior. The proposed control frameworkdetermines the optimal sequence and duration for the control strategy based on actual plant conditions and state predictions and computes the optimal control moves. It also determines the permeate flow that has to be reached by the control of the membrane modules.The objective for the membrane modules is to realize the permeate flow determined by the control strategy applied for the biological part of the plant while simultaneously minimizing the operational costs for the membrane modules. The model used for this optimization is based on simple measurements available at the membrane module. A second objective is to calculate the maximum possible flow that can be realized under the current membraneblocking. This information is needed to give an upper limit for the optimization of the biological part.The developed model based optimization framework is realized in a realtime environment and tested in simulation studies. The model used for the simulation studies is developed from literature and presents a highly detailed model including biological waste degradation based on the Activated Sludge Model No. 3, climate induced temperature variations, cake formation, the effect of the aeration on the cake formation and the pore blocking resultingfrom fouling.The presented results show, that the control strategy can be adopted based on the surrounding scenario. The main savings for the operational costs of this type of plant can be achieved byreducing the aeration of the membrane modules.