For cost and reliability efficiency, optimal design and operation of pressurized water distribution networks is highly important. However, optimizing such networks is still a challenge since it requires an appropriate determination of: (1) dimension of pipe / pump / tank - decision variables (2) cost / network reliability - objective functions and (3) limits or restrictions within which the network must operate - a given set of constraints. The costs mentioned here consist in general of capital, construction, and operation costs. The reliability of a network mainly refers to the intrinsic capability of providing water with adequate volume and a certain pressure to consumers under normal and extreme conditions. These contradicting objective functions are functions of network configuration regarding component sizes and network layout. Because considerable uncertainties finally render the overall task to a highly complex problem, most recent approaches mainly focus only on finding a trade-off between minimizing cost and maximizing network reliability. To overcome these limitations, a novel model system that simultaneously considers network configuration, its operation and the relevant uncertainties is proposed in this study. For solving this multi-objective design problem, a simulation-based optimization approach has been developed and applied. The approach couples a hydraulic model (Epanet) with the covariance matrix adaptation evolution strategy (CMA-ES) and can be operated in two different modes. These modes are (1) simulation–based Single-objective optimization and (2) simulation-based multi-objective optimization. Single-objective optimization yields the single best solution with respect to cost or network reliability, whereas multi-objective optimization produces a set of non-dominated solutions called Pareto optimal solutions which are trade-offs between cost and reliability. In addition, to prevent a seriously under-designed network, demand uncertainties was also taken into account through a so called “robustness probability” of the network. This consideration may become useful for a more reliable water distribution network. In order to verify the performance of the proposed approach, it was systematically tested on a number of different benchmark water distribution networks ranging from simple to complex. These benchmark networks are either gravity-fed or pumped networks which need to be optimally designed to supply urban or irrigation water demand under specific constraints. The results show that the new approach is able: • to solve optimization problems of pressurized water distribution network design and operation regarding cost and network reliability; • to directly determine the pumping discharge and head, thus allowing to select pumps more adequately; • to simulate time series of tank water level; • to eliminate redundant pipes and pumps to generate an optimal network layout; • to respond well to complex networks other than only to simple networks; • to perform with multiple demand loading; • to produce reliable Pareto optimal solutions regarding multi-objective optimization. In conclusion, the new technique can be successfully applied for optimization problems in pressurized water distribution network design and operation. The new approach has been demonstrated to be a powerful tool for optimal network design not only for irrigation but also for an urban water supply.

Lagegeregelte Bewegungsachsen finden sich in nahezu allen aktuellen Werkzeug‐ und Produktionsmaschinen. Um eine hohe Qualität der hergestellten Produkte bei gleichzeitig möglichst hoher Effizienz sicherzustellen muss die Regelung der Bewegungsachsen genau auf das Regelstreckenverhalten abgestimmt sein. Informationen über die Regelstrecken können mit Verfahren der Identifikation ermittelt werden. Neben der Reglerparametrierung sind diese Informationen ebenfalls für Mechanismen der Maschinenüberwachung relevant, bei denen Fehlerzustände in den Maschinen und Anlagen (modellbasiert) frühzeitig erkannt werden sollen. Neben den Identifikationsmechanismen, welche derzeit im Bereich der Werkzeugmaschinen vorherrschend sind und meist auf Testsignalen basieren, können mit nichtinvasive Ansätzen wesentliche Parameter bestimmt werden, ohne in den Produktionsprozess einzugreifen zu müssen. Im vorliegend Beitrag wird dargestellt, wie derartige Verfahren im Umfeld der Werkzeugmaschine Anwendung finden können. Nach einer Einleitung wird im Beitrag der Stand der Technik zur Identifikation von Regelstreckenparametern an Bewegungsachsen beleuchtet. Das Hauptaugenmerk liegt dabei in der Unterscheidung von invasiven (Verfahren mit Testsignalen) und nichtinvasiven Identifikationsverfahren (ohne Testsignale). Aufbauend auf den Erkenntnissen der Literaturrecherche wird das eigene Vorgehen hinsichtlich Wahl der Modellstruktur und des Identifikationsverfahrens erläutert. Ausführungen zur Anregungsdetektion, gezielten Einflussnahe auf die Parameterschätzung, Fehlerbewertung und dem Einsatz der simulationsbasierten Optimierung schließen die theoretischen Betrachtungen zum Forschungsthema ab. Die erzielten Ergebnisse werden in einem weiteren Kapitel anhand eines Laborversuchsstandes und einer Werkzeugmaschine illustriert. Der letzte Abschnitt gibt eine Zusammenfassung des Forschungsvorhabens und erläutert zukünftige Schritte auf diesem Gebiet.

For cost and reliability efficiency, optimal design and operation of pressurized water distribution networks is highly important. However, optimizing such networks is still a challenge since it requires an appropriate determination of: (1) dimension of pipe / pump / tank - decision variables (2) cost / network reliability - objective functions and (3) limits or restrictions within which the network must operate - a given set of constraints. The costs mentioned here consist in general of capital, construction, and operation costs. The reliability of a network mainly refers to the intrinsic capability of providing water with adequate volume and a certain pressure to consumers under normal and extreme conditions. These contradicting objective functions are functions of network configuration regarding component sizes and network layout. Because considerable uncertainties finally render the overall task to a highly complex problem, most recent approaches mainly focus only on finding a trade-off between minimizing cost and maximizing network reliability. To overcome these limitations, a novel model system that simultaneously considers network configuration, its operation and the relevant uncertainties is proposed in this study. For solving this multi-objective design problem, a simulation-based optimization approach has been developed and applied. The approach couples a hydraulic model (Epanet) with the covariance matrix adaptation evolution strategy (CMA-ES) and can be operated in two different modes. These modes are (1) simulation–based Single-objective optimization and (2) simulation-based multi-objective optimization. Single-objective optimization yields the single best solution with respect to cost or network reliability, whereas multi-objective optimization produces a set of non-dominated solutions called Pareto optimal solutions which are trade-offs between cost and reliability. In addition, to prevent a seriously under-designed network, demand uncertainties was also taken into account through a so called “robustness probability” of the network. This consideration may become useful for a more reliable water distribution network. In order to verify the performance of the proposed approach, it was systematically tested on a number of different benchmark water distribution networks ranging from simple to complex. These benchmark networks are either gravity-fed or pumped networks which need to be optimally designed to supply urban or irrigation water demand under specific constraints. The results show that the new approach is able: • to solve optimization problems of pressurized water distribution network design and operation regarding cost and network reliability; • to directly determine the pumping discharge and head, thus allowing to select pumps more adequately; • to simulate time series of tank water level; • to eliminate redundant pipes and pumps to generate an optimal network layout; • to respond well to complex networks other than only to simple networks; • to perform with multiple demand loading; • to produce reliable Pareto optimal solutions regarding multi-objective optimization. In conclusion, the new technique can be successfully applied for optimization problems in pressurized water distribution network design and operation. The new approach has been demonstrated to be a powerful tool for optimal network design not only for irrigation but also for an urban water supply.

Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised: (i) Is this approach a suitable strategy to improve WP; (ii) Is the sensor for irrigation control suitable; (iii) Which crop growth models are suitable to be part of that approach; and (iv) Can the combined application with experiments prove an increase of WP? The stochastic simulation-optimization approach allowed deriving parameter values for an optimal irrigation control for sensor-based full and deficit irrigation strategies. Objective was to achieve high WP with high reliability. Parameters for irrigation control included irrigation thresholds of soil-water potentials because of the working principle behind plant transpiration where pressure gradients are transmitted from the air through the plant and into the root zone. Optimal parameter values for full and deficit irrigation strategies were tested in irrigation experiments in containers in a vegetation hall with drip irrigated maize and compared to schedule-based irrigation strategies with regard to WP and water consumption. Observation data from one of the treatments was used afterwards in a simulation study to systematically investigate the parameters for implementing effective setups of DI systems. The combination of simulation-optimization and irrigation experiments proved to be a suitable approach for investigating and improving WP, as well as for deriving optimal parameter values of different irrigation strategies. This was verified in the irrigation experiment and shown through overall high WP, equally high WP between deficit and full irrigation strategies, and achieved water savings. Irrigation thresholds beyond the measurement range of tensiometers are feasible and applicable. The pF-meter performed satisfactorily and is a promising candidate for irrigation control. Suitable crop models for being part of this approach were found and their properties formulated. Factors that define the behavior of DI systems regarding WP and water consumption were investigated and assessed. This research allowed for drawing the first conclusions about the potential range of operations of sensor-based DI systems for achieving high WP with high reliability through its systematical investigation of such systems. However, this study needs validation and is therefore limited with regard to exact values of derived thresholds.

Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised: (i) Is this approach a suitable strategy to improve WP; (ii) Is the sensor for irrigation control suitable; (iii) Which crop growth models are suitable to be part of that approach; and (iv) Can the combined application with experiments prove an increase of WP? The stochastic simulation-optimization approach allowed deriving parameter values for an optimal irrigation control for sensor-based full and deficit irrigation strategies. Objective was to achieve high WP with high reliability. Parameters for irrigation control included irrigation thresholds of soil-water potentials because of the working principle behind plant transpiration where pressure gradients are transmitted from the air through the plant and into the root zone. Optimal parameter values for full and deficit irrigation strategies were tested in irrigation experiments in containers in a vegetation hall with drip irrigated maize and compared to schedule-based irrigation strategies with regard to WP and water consumption. Observation data from one of the treatments was used afterwards in a simulation study to systematically investigate the parameters for implementing effective setups of DI systems. The combination of simulation-optimization and irrigation experiments proved to be a suitable approach for investigating and improving WP, as well as for deriving optimal parameter values of different irrigation strategies. This was verified in the irrigation experiment and shown through overall high WP, equally high WP between deficit and full irrigation strategies, and achieved water savings. Irrigation thresholds beyond the measurement range of tensiometers are feasible and applicable. The pF-meter performed satisfactorily and is a promising candidate for irrigation control. Suitable crop models for being part of this approach were found and their properties formulated. Factors that define the behavior of DI systems regarding WP and water consumption were investigated and assessed. This research allowed for drawing the first conclusions about the potential range of operations of sensor-based DI systems for achieving high WP with high reliability through its systematical investigation of such systems. However, this study needs validation and is therefore limited with regard to exact values of derived thresholds.

Lagegeregelte Bewegungsachsen finden sich in nahezu allen aktuellen Werkzeug‐ und Produktionsmaschinen. Um eine hohe Qualität der hergestellten Produkte bei gleichzeitig möglichst hoher Effizienz sicherzustellen muss die Regelung der Bewegungsachsen genau auf das Regelstreckenverhalten abgestimmt sein. Informationen über die Regelstrecken können mit Verfahren der Identifikation ermittelt werden. Neben der Reglerparametrierung sind diese Informationen ebenfalls für Mechanismen der Maschinenüberwachung relevant, bei denen Fehlerzustände in den Maschinen und Anlagen (modellbasiert) frühzeitig erkannt werden sollen. Neben den Identifikationsmechanismen, welche derzeit im Bereich der Werkzeugmaschinen vorherrschend sind und meist auf Testsignalen basieren, können mit nichtinvasive Ansätzen wesentliche Parameter bestimmt werden, ohne in den Produktionsprozess einzugreifen zu müssen. Im vorliegend Beitrag wird dargestellt, wie derartige Verfahren im Umfeld der Werkzeugmaschine Anwendung finden können. Nach einer Einleitung wird im Beitrag der Stand der Technik zur Identifikation von Regelstreckenparametern an Bewegungsachsen beleuchtet. Das Hauptaugenmerk liegt dabei in der Unterscheidung von invasiven (Verfahren mit Testsignalen) und nichtinvasiven Identifikationsverfahren (ohne Testsignale). Aufbauend auf den Erkenntnissen der Literaturrecherche wird das eigene Vorgehen hinsichtlich Wahl der Modellstruktur und des Identifikationsverfahrens erläutert. Ausführungen zur Anregungsdetektion, gezielten Einflussnahe auf die Parameterschätzung, Fehlerbewertung und dem Einsatz der simulationsbasierten Optimierung schließen die theoretischen Betrachtungen zum Forschungsthema ab. Die erzielten Ergebnisse werden in einem weiteren Kapitel anhand eines Laborversuchsstandes und einer Werkzeugmaschine illustriert. Der letzte Abschnitt gibt eine Zusammenfassung des Forschungsvorhabens und erläutert zukünftige Schritte auf diesem Gebiet.

Wasserwirtschaftliche Speichersysteme sind unverzichtbar, um weltweit die Trinkwasserversorgung, Nahrungsmittelproduktion und Energieversorgung sicherzustellen. Die multikriterielle simulationsbasierte Optimierung (MK-SBO) ist eine leistungsfähige Methodik, um für Mehrzweck-Talsperrenverbundsysteme (MZ-TVS) eine Pareto-optimale Menge an Kompromisslösungen zwischen konträren Zielen bereitzustellen. Der rechentechnische Aufwand steigt jedoch linear mit der Länge des Simulationszeitraums der Talsperrenbewirtschaftung an. Folglich begrenzen sich MK-SBO-Studien bisher auf Simulationszeiträume von wenigen Jahrzehnten. Diese Zeiträume sind i.d.R. unzureichend, um Unsicherheiten, die aus der stochastischen Natur der Zuflüsse resultieren, adäquat zu beschreiben. Bewirtschaftungsoptimierungen von MZ-TVS hinsichtlich ihrer Zuverlässigkeit, z.B. durch die Maximierung von Versorgungssicherheiten, können sich als wenig belastbar und ermittelte Steuerungsstrategien als wenig robust erweisen. Um diesen Herausforderungen zu begegnen, wird ein neues modulares Framework zur multikriteriellen simulationsbasierten Bewirtschaftungsoptimierung von MZ-TVS (Frams-BoT) entwickelt. Eine Informationserweiterung zu stochastischen Zuflussprozessen erfolgt über ein weiterentwickeltes Zeitreihenmodell mittels generierter Zeitreihen von mehreren Tausend Jahren Länge. Eine neue Methode zur Monte-Carlo-Rekombination von Zeitreihen ermöglicht dann die Nutzung dieser Informationen in der MK-SBO in wesentlich kürzeren Simulationszeiträumen. Weitere Rechenzeit wird durch Parallelisierung und eine fortgeschrittene Kodierung von Entscheidungsvariablen eingespart. Die Simulation von Zuflussdargeboten für multikriterielle Klimafolgenanalysen erfolgt durch ein prozessorientiertes Wasserhaushaltsmodell. Level-Diagramme (Blasco et al., 2008) unterstützten den komplexen Prozess der Entscheidungsfindung. Die Wirksamkeit und Flexibilität des Frameworks wurden in zwei Fallstudien gezeigt. In einer ersten Fallstudie konnten in einer Klimafolgenanalyse Versorgungssicherheiten von über 99% als ein Ziel eines multikriteriellen Optimierungsproblems maximiert werden, um die Verlässlichkeit der Bewirtschaftung eines MZ-TVS in Sachsen (Deutschland) zu steigern. Eine zweite Fallstudie befasste sich mit der Maximierung der Leistungsfähigkeit eines MZ-TVS in Äthiopien unter verschiedenen Problemformulierungen. In beiden Fallstudien erwiesen sich die erzielten Pareto-Fronten und Steuerungsstrategien gegenüber 10 000-jährigen Zeiträumen als robust. Die benötigten Rechenzeiten der MK-SBO ließen sich durch das Framework massiv senken. / Water resources systems are worldwide essential for a secure supply of potable water, food and energy production. Simulation-based multi-objective optimization (SB-MOO) is a powerful method to provide a set of Pareto-optimal compromise solutions between various contrary goals of multi-purpose multi-reservoir systems (MP-MRS). However, the computational costs increases with the length of the time period in which the reservoir management is simulated. Consequently, MK-SBO studies are currently restricted to simulation periods of several decades. These time periods are normally insufficient to describe the stochastic nature of the inflows and the consequent hydrological uncertainties. Therefore, an optimization of the reliability of management of MP-MRS, e.g. through the maximization of the security of supply, may not be resilient. Obtained management strategies may not prove robust. To address these challenges, a new modular framework for simulation-based multiobjective optimization of the reservoir management of multi-purpose multi-reservoir systems (Frams-BoT) is developed. A refined time series model provides time series of several thousand years to extend the available information about the stochastic inflow processes. Then, a new Monte-Carlo recombination method allows for the exploitation of the extended information in the SB-MOO on significantly shorter time periods. Further computational time is saved by parallelization and an advanced coding of decision variables. A processoriented water balance model is used to simulate inflows for multi-objective climate impact analysis. Level-Diagrams [Blasco et al., 2008] are used to support the complex process of decision-making. The effectiveness and flexibility of the framework is presented in two case studies. In the first case study about a MP-MRS in Germany, high securities of supply over 99% where maximized as part of a multi-objective optimization problem in order to improve the reliability of the reservoir management. A second case study addressed the maximization of the performance of a MP-MRS in Ethiopia under different formulations of the optimization problem. In both case studies, the obtained Pareto-Fronts and management strategies proved robust compared to 10 000 year time periods. The required computational times of the SB-MOO could be reduced considerably.

Wasserwirtschaftliche Speichersysteme sind unverzichtbar, um weltweit die Trinkwasserversorgung, Nahrungsmittelproduktion und Energieversorgung sicherzustellen. Die multikriterielle simulationsbasierte Optimierung (MK-SBO) ist eine leistungsfähige Methodik, um für Mehrzweck-Talsperrenverbundsysteme (MZ-TVS) eine Pareto-optimale Menge an Kompromisslösungen zwischen konträren Zielen bereitzustellen. Der rechentechnische Aufwand steigt jedoch linear mit der Länge des Simulationszeitraums der Talsperrenbewirtschaftung an. Folglich begrenzen sich MK-SBO-Studien bisher auf Simulationszeiträume von wenigen Jahrzehnten. Diese Zeiträume sind i.d.R. unzureichend, um Unsicherheiten, die aus der stochastischen Natur der Zuflüsse resultieren, adäquat zu beschreiben. Bewirtschaftungsoptimierungen von MZ-TVS hinsichtlich ihrer Zuverlässigkeit, z.B. durch die Maximierung von Versorgungssicherheiten, können sich als wenig belastbar und ermittelte Steuerungsstrategien als wenig robust erweisen. Um diesen Herausforderungen zu begegnen, wird ein neues modulares Framework zur multikriteriellen simulationsbasierten Bewirtschaftungsoptimierung von MZ-TVS (Frams-BoT) entwickelt. Eine Informationserweiterung zu stochastischen Zuflussprozessen erfolgt über ein weiterentwickeltes Zeitreihenmodell mittels generierter Zeitreihen von mehreren Tausend Jahren Länge. Eine neue Methode zur Monte-Carlo-Rekombination von Zeitreihen ermöglicht dann die Nutzung dieser Informationen in der MK-SBO in wesentlich kürzeren Simulationszeiträumen. Weitere Rechenzeit wird durch Parallelisierung und eine fortgeschrittene Kodierung von Entscheidungsvariablen eingespart. Die Simulation von Zuflussdargeboten für multikriterielle Klimafolgenanalysen erfolgt durch ein prozessorientiertes Wasserhaushaltsmodell. Level-Diagramme (Blasco et al., 2008) unterstützten den komplexen Prozess der Entscheidungsfindung. Die Wirksamkeit und Flexibilität des Frameworks wurden in zwei Fallstudien gezeigt. In einer ersten Fallstudie konnten in einer Klimafolgenanalyse Versorgungssicherheiten von über 99% als ein Ziel eines multikriteriellen Optimierungsproblems maximiert werden, um die Verlässlichkeit der Bewirtschaftung eines MZ-TVS in Sachsen (Deutschland) zu steigern. Eine zweite Fallstudie befasste sich mit der Maximierung der Leistungsfähigkeit eines MZ-TVS in Äthiopien unter verschiedenen Problemformulierungen. In beiden Fallstudien erwiesen sich die erzielten Pareto-Fronten und Steuerungsstrategien gegenüber 10 000-jährigen Zeiträumen als robust. Die benötigten Rechenzeiten der MK-SBO ließen sich durch das Framework massiv senken. / Water resources systems are worldwide essential for a secure supply of potable water, food and energy production. Simulation-based multi-objective optimization (SB-MOO) is a powerful method to provide a set of Pareto-optimal compromise solutions between various contrary goals of multi-purpose multi-reservoir systems (MP-MRS). However, the computational costs increases with the length of the time period in which the reservoir management is simulated. Consequently, MK-SBO studies are currently restricted to simulation periods of several decades. These time periods are normally insufficient to describe the stochastic nature of the inflows and the consequent hydrological uncertainties. Therefore, an optimization of the reliability of management of MP-MRS, e.g. through the maximization of the security of supply, may not be resilient. Obtained management strategies may not prove robust. To address these challenges, a new modular framework for simulation-based multiobjective optimization of the reservoir management of multi-purpose multi-reservoir systems (Frams-BoT) is developed. A refined time series model provides time series of several thousand years to extend the available information about the stochastic inflow processes. Then, a new Monte-Carlo recombination method allows for the exploitation of the extended information in the SB-MOO on significantly shorter time periods. Further computational time is saved by parallelization and an advanced coding of decision variables. A processoriented water balance model is used to simulate inflows for multi-objective climate impact analysis. Level-Diagrams [Blasco et al., 2008] are used to support the complex process of decision-making. The effectiveness and flexibility of the framework is presented in two case studies. In the first case study about a MP-MRS in Germany, high securities of supply over 99% where maximized as part of a multi-objective optimization problem in order to improve the reliability of the reservoir management. A second case study addressed the maximization of the performance of a MP-MRS in Ethiopia under different formulations of the optimization problem. In both case studies, the obtained Pareto-Fronts and management strategies proved robust compared to 10 000 year time periods. The required computational times of the SB-MOO could be reduced considerably.