Dissertations

Advice and supervision: Prof. Dr. Martin Faulstich

Advice and supervision: Prof. Dr. Martin Faulstich

Moving toward sustainability entails optimizing energy consumption through improving efficiency and maximizing the percentage of renewable energy use for human activities. Urban planning can play an important role in this favor. Although, the importance of transportation system and land-use distribution with a focus on the impact of transportation sector on the energy use have been vigorously studied, the association between urban form and residential energy use is still ambivalent; Thus, this proposed research aims to study the urban form variables which have significant impacts on residential energy use and explore how they are related in Tehran and Dortmund as case studies. Therefore, this research randomly samples individual households in these two cities based on Cochran sample size formula and explores the impact of urban form variables through individual households as well as their neighborhoods.

The structure model would be hierarchical, with households nested within their neighborhoods, cities and states. In other words, indicators hierarchically related to each other which must be considered by the model; therefore, Multilevel Modeling (MLM) is selected as the computational method. Although the term Residential Energy Demand refers to lighting, appliances, cooling, heating and other subsectors, the highest demand belongs to the energy used for the purposes of cooling and heating. Therefore, this proposed research will focus mainly on cooling and heating energy consumption. Some urban form variables are expected to have reversal behavior in this regard. Thus, another contribution of this research would be following the trade-off between these variables and the energy use in the sample cities with a consideration of their climatic, geographical and cultural features.

Advice and supervision: Prof. Dr. Martin Faulstich

The construction industry is one of the most resource-intensive sectors of the economy and is responsible for a large proportion of global greenhouse gas emissions. The construction industry is also one of the largest producers of waste, particularly through the demolition of buildings and roads. Mineral construction and demolition waste accounts for more than half of the total waste stream generated in Germany. The construction and demolition waste produced can be reused as so-called recycled construction materials. However, the largest proportion is used in downcycling processes. Due to population growth and urbanization, construction activities will continue to increase worldwide in the coming years, which will also increase the demand for raw materials. This raises the question of how more effective management of mineral construction and demolition waste can be implemented.

Partner: Westnetz GmbH

Consulting and supervision: Prof. Dr. Martin Faulstich

The European Union's greenhouse gas reduction targets require far-reaching transformations within the entire energy system. The use of hydrogen is a promising option for decarbonization, especially in industrial processes. Due to the influence of various dynamic factors (future energy demand, climate impacts, etc.) in conjunction with a less flexible and cost-intensive pipeline system, the planning for the initial development of such a regional hydrogen infrastructure poses a major challenge and is not possible with the help of current network planning tools. In order to solve this challenge, the doctoral thesis will show how a regional hydrogen infrastructure consisting of generation plants, pipeline networks and various supply plants can be set up, taking into account the requirements for sustainable spatial development. This will be done by modeling the hydrogen infrastructure in the Sauerland region, taking into account spatial suitability and resistance analyses. In doing so, points relating to the security of supply of energy infrastructure will also be linked to the concept of sustainability in spatial planning in order to further develop the interdisciplinary understanding of sustainability. Based on the results of scenario analyses and the resulting hydrogen infrastructure planning for the region, general guidelines for grid planning will be formulated to support the development of sustainable energy infrastructure in the future

Partner: eew Energy from Waste GmbH, Helmstadt
Advice and support: Prof. Dr. Martin Faulstich

According to the climate protection plans of the Federal Environment Ministry for the decarbonization of the heat, electricity and fuel sectors, waste incineration plants can make an important contribution by producing largely climate-neutral electricity and heat. Despite modern control, automation and measurement technology, the efficient and optimal operation of a waste incineration plant in compliance with the requirements such as combustion temperatures, retention times and emission limits according to the 17th Ordinance on the Implementation of the Federal Immission Control Act (BImSchV) is still a process engineering challenge due to the inhomogeneity of the waste composition. In this research project, an AI model of a waste incineration plant is developed on the basis of modern AI algorithms such as machine learning and neural networks, which learns the processes of a waste incineration plant and provides new correlations between the target variables of combustion, pollution, throughput, emissions and efficiency. The scientifically validated findings are then integrated into the conventional control process of the plant using a standard procedure developed in-house in order to solve vector optimization. The AI model is developed using the Python programming language. Machine learning can be divided into four main areas based on the way the data is monitored: Supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The types of learning mentioned above are examined in the context of the research objective. The strengths and weaknesses of individual algorithms are worked out according to specific quality criteria so that the validated AI model represents a combination of all AI algorithms.

Partner: HP Enterprises / Circular Economy Research
Advice and support: Prof. Dr. Martin Faulstich

Partner: Kaiser Ingenieure
Advice and support: Dr. Matthias Kaiser and Prof. Dr. Martin Faulstich

Urban flash flooding is a growing problem today. Two key developments are responsible for the increase in the problem. On the one hand, there has been an increase in heavy rainfall events and flooding in recent years. On the other hand, more than half of the world's population already lives in urban areas - with strong growth expected in the future. If this growth is accompanied by an increase in prosperity, not only the population but also material assets will be increasingly concentrated in smaller areas. This growing potential for damage also increases the severity of the consequences of flooding. Considering that 90 percent of the increase in urban population by 2050 will take place in Asia and Africa, it can be assumed that urban heavy rainfall prevention will become massively more important, especially in these regions.

It can therefore be assumed that heavy rain hazard maps will also become much more important in the future. Due to the expected increase in demand for heavy rain hazard maps, the call for new methods for creating heavy rain hazard maps is also likely to become louder quickly, as current methods have a number of weaknesses. These usually consist of a very high computational effort as well as the required technical expertise and the high manual effort to remove errors from the model. These resources are often not available in the municipal environment. Specialized companies are faced with the challenge of delivering sufficiently high quality despite high price and competitive pressure. This is particularly critical in view of the high manual workload required for the hydrological correction of common models.

Against this background and taking into account the technical developments of recent years, a number of new approaches are emerging. Buzzwords in this context are big data, open data, artificial intelligence, deep learning, neural networks, machine learning, etc.

Artificial intelligence (AI), mostly based on artificial neural networks (ANN), is already performing a range of tasks. Speech and image recognition are common examples of this. In the field of image recognition in particular, the topic has recently also found its way into the field of geoinformation systems. The aim of the dissertation is to examine these technologies in the context of the creation of heavy rain hazard maps and to investigate their implementation in planning practice.

Partner: Federal Environment Agency
Advice and supervision: Prof. Dr. Martin Faulstich
Title: "Material limits of the circular economy - status and perspectives of the circular economy under characterization, modelling and evaluation of the material balance with special appreciation of stock dynamics in Germany's anthropogenic stock"

The circular economy is increasingly seen in political programs as a consistent solution for a sustainable, resource-conserving economy. The central paradigm of the circular economy is the preservation of the value and function of goods, products and materials in the economic area. Accordingly, an ideal-typical raw material-using system in a steady state should be largely self-sufficient in secondary raw materials and be able to dispense almost entirely with primary raw materials. However, real systems such as national economies are characterized by a complex, dynamic material interdependence with losses and veritable raw material consumption. Recycling is subject to technological and economic barriers. In addition, these systems not only experience saturation effects, but also considerable growth and shrinkage dynamics in the anthropogenic stock. There is a large time lag between placing relevant groups of goods on the market and their disposal. Against this background, where are realistic material target corridors in a circular economy and where are the limits? Which materials can be managed in cycles at all and which indicators can be used to reliably describe the development of material life cycles? To what extent will the supply of primary raw materials remain necessary? The doctoral student is addressing these key questions and investigating the degree of circularity that will be possible for various materials under current and expected future technical conditions.

Partners: Berlin Institute for Population and Development, Berlin; Daimler Benz Foundation
Advice and support: Prof. Dr. Martin Faulstich

The central challenge for the early industrialized countries in the coming decades will be to steer their societies and economic systems onto a sustainable path. The hope that technical progress with more efficient production processes will lead to less environmental impact has not yet been fulfilled - despite the partial decoupling of raw material consumption and economic growth. Although a unit of GDP can now be generated with less environmental and nature consumption than in the past, the economic growth of recent years has led to more raw material consumption and environmental damage in absolute terms. However, irrespective of this trend, there have been signs of a slowdown in growth in industrialized countries for several decades. Some countries, such as Japan, are experiencing almost zero cyclically-adjusted growth. The theory of secular stagnation provides an explanation for this development. It blames dwindling economic growth on structural and irreversible factors such as demographic change and declining productivity gains. If the theory of secular stagnation is correct, more and more early developed countries will experience declining economic growth or none at all. This raises the question of whether the equation "more economic growth = more environmental damage" also works in reverse. There is still a considerable need for research in this area. The doctoral project "Secular stagnation and its ecological consequences" is therefore dedicated to the following questions: What would be the ecological consequences of a permanent end to economic growth in the early developed countries? Can secular stagnation bring industrialized countries closer to their climate goals and, if so, how great would the ecological dividend be that could be derived from this?

Advice and supervision: Prof. Dr. Martin Faulstich

The spatial planning system in Germany is based on three levels: Federal, state and regional. According to §1 ROG, federal spatial planning provides the legal basis for state planning based on guiding principles and principles. State planning concretizes these guidelines before regional planning adapts them to the regional parameters. The European level, which provides binding guidelines, is superordinate to this. German spatial planning has a large number of planning instruments that have already been in use for several years, but the effectiveness of regional spatial planning is rarely evaluated once the process has been completed.

This raises the question of the extent to which nationwide strategies for renewable energies diffuse across the various planning levels and are reflected in spatial developments. Part of this study is the identification of projects and regions that are considered best and worst practice examples in the implementation of federal and state strategies as well as renewable energy projects and grid expansion. This information will be used to formulate a catalog of measures for regional planning communities with examples of successful implementation of the federal targets.