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Steel is an important engineering material used in a variety of applications due to its mechanical properties and durability. With increasing demand for higher performance, complex structures, and the need for cost reduction within manufacturing processes, there are numerous challenges with traditional steel design options and production methods with manufacturing cost being the most significant. In this research, this challenge is addressed by developing a micro-genetic algorithm to minimize the manufacturing cost while designing steel with the desired mechanical properties. The algorithm was integrated with machine learning models to predict the mechanical properties and microstructure for the generated alloys based on their chemical compositions and heat treatment conditions. Through this, it was demonstrated that new steel alloys with specific mechanical property targets could be generated at an optimal cost. The research’s contribution lies in the development of a different approach to optimize steel production that combines the advantages of machine learning and evolutionary algorithms while increasing the number of input parameters. Additionally, it uses a small dataset illustrating that it can be used in applications where data is lacking. This approach has significant implications for the steel industry as it provides a more efficient way to design and produce new steel alloys. It also contributes to the overall material science field by demonstrating its ability in a material’s design and optimization. Overall, the proposed framework highlights the potential of utilizing machine learning and evolutionary algorithms in material design and optimization. / Thesis / Master of Applied Science (MASc) / This research aims to develop an AI-based functional integrated with a heuristic algorithm that optimizes parameters to meet desired mechanical properties and cost for steels. The developed algorithm generates new alloys which meet desired mechanical property targets by considering alloy composition and heat treatment condition inputs. Used in combination with machine learning models for the mechanical property and microstructure prediction of new alloys, the algorithm successfully demonstrates its ability to meet specified targets while achieving cost savings. The approach presented has significant implications for the steel industry as it offers a quick method of optimizing steel production, which can reduce overall costs and improve efficiency. The integration of machine learning within the algorithm offers a different way of designing new steel alloys which has the potential to improve manufactured products by ultimately improving their performance and quality.

Climate change (CC) science has made significant progress in development of predictive models. Despite these recent advances, the assessment of CC impacts in urban watersheds remains an area of active research, in part due to the small temporal and spatial scales needed to adequately characterize urban systems. Urban watersheds have been the focus of considerable efforts to restore hydrology and water quality, and the aquatic habitat of receiving waters, yet CC impacts threaten to reduce the effectiveness of these efforts. Thus, assessing the impacts of CC in urban watershed assessment are essential for assuring the success of water quality improvement programs and is an important research need. Simulations of CC for the 2041-2068 period were developed using downscaled Global Climate Models (GCMs) from the North American Regional CC Assessment Program (NARCCAP) and Coupled Model Intercomparison Project Phase 5 (CMIP5) to forecast precipitation and temperature time series. This data were then used to force a Storm Water Management Model (SWMM) of the Difficult Run watershed of Fairfax County, Virginia, a tributary of Potomac River, which flows into Chesapeake Bay. NARCCAP uses a scenario represents a medium-high greenhouse gas emissions assumption, A2; the latter, uses five GCMs, and two Representative Concentration Pathways (RCP 4.5 and 8.5) scenarios in an ensemble approach to better assess variability of model predictions in presenting precipitation, temperature, runoff quantity and quality. Then, the effects of CC on runoff peak, volume, and nutrient and sediment loads delivered to the Chesapeake Bay and on the treatment performance of a very common stormwater control measure (SCM), retention ponds, was assessed. Rainwater Harvesting (RWH) systems are an unusual SCM in that they recycle and reuse stormwater, normally from rooftops, and increase water supply and reduce runoff. The efficiency of RWH systems for projected CC for these dual purposes was assessed. NARCAAP data for selected locations across the U.S. were statistically downscaled using a modified version of the equiratio cumulative distribution function matching method to create a time series of projected precipitation and temperature. These data were used to force a simulation model, the Rainwater Analysis and Simulation Program (RASP) to assess the impacts of CC on RWH with respect to the reliability of water supply and runoff capture. To support CC modeling, an easy-to-use software tool, RSWMM-Cost, was developed. RSWMM-Cost automates the execution of SWMM, which is commonly used for simulating urban watersheds. Several features were incorporated into the RSWMM-Cost tool, including automated calibration, sensitivity analysis, and cost optimization modules; the latter can assist in identifying the most cost-effective combination of SCMs in an urban watershed. As an example, RSWMM-Cost was applied to a headwater subcatchment the Difficult Run watershed. / Ph. D. / Urban watersheds have been the focus of considerable efforts to restore water quantity and quality, and the aquatic habitat of receiving waters, yet climate change impacts threaten to reduce the effectiveness of these efforts. The assessment of climate change impacts in urban watersheds remains an area of active research, in part due to the small temporal and spatial scales needed to adequately characterize urban systems. Thus, assessing the impacts of climate change in urban watershed assessment are essential for assuring the success of water quality improvement programs and is an important research need. In this study, simulations of climate change for the 2041-2068 period were developed to forecast precipitation and temperature data. These data were then used to force a hydrologic model for the Difficult Run watershed of Fairfax County, Virginia, a tributary of Potomac River, which flows into Chesapeake Bay. Then, the effects of climate change on runoff, nutrient and sediment loads delivered to the Chesapeake Bay and on the treatment efficiency of a very common management practice called retention ponds, was assessed. Rainwater harvesting systems are an unusual management practice that recycle and reuse stormwater, normally from rooftops, and increase water supply and reduce runoff. The efficiency of rainwater harvesting systems for projected climate change with respect to the reliability of water supply and runoff capture was assessed for the 2041-2068 period. To support climate change modeling, an easy-to-use tool, was also developed to select the most cost-optimized combination of best management practices in urban watersheds considering site constraints, limitations, and size. As an example, the tool was applied to a headwater subcatchment of the Difficult Run watershed. The ability to assess the impact of climate change on both hydrologic and water quality treatment could assist in the selection of the most appropriate management practices to address water management goals and conserve limited financial resources.

The thesis is concerned with a question, what plays the key role in decision making process about restructuring of production capacities of automotive companies within global process. All of this is running in the background of optimization factors influencing their cost items value. Volkswagen AG, more precisely SKODA AUTO was chosen for behavior analysis introduced as a case study. The goal is to suggest some recommendations for SKODA AUTO to help with achieving sales of 1,5 million cars in 2018 based on global pressures analysis affecting automotive industry and future cost development study in this field. The main benefit of the thesis is the complex cost items analysis resulting in application of the conclusions into SKODA AUTO case in a form of recommendations based on cost reduction.

This diploma thesis deals with spot energy trading in the Czech Republic. The first section focuses on legal market environments, market members, and types of individual markets. This serves as an introduction to the second and the final section, which answers the question of the advantageousness of spot energy trading. The final section illustrates a practical demonstration of an optimization analysis of energy costs in a particular company. The contribution of this thesis, in my opinion, is the practical description of the calculation of energy costs in one company or an association of companies, which are considering the spot energy purchase, and a transfer of the responsibility for the deviation.

Adoption of electric vehicles has been restrained by the availability of charging stations and consumer fear of being stranded with a depleted battery, far from the nearest charger. In many areas of the world, charging stations are now widely available and the transition from vehicles with internal combustion engines is accelerating, though still in a fairly early stage. For electric vehicle drivers in those areas, anxiety that they will not be able to find a charger (“range anxiety”) is subsiding. However, differences in charging speed and pricing between stations and different outlets at the same station can be large. Total trip duration can vary significantly based on the charging outlet selected. Prior research has developed methods for helping all drivers find the fastest route and for electric vehicle drivers to ensure that they are able to complete their trip. Additional research has explored other complexities of route selection for electric vehicles such as how to select optimal stations for charging based on the total trip duration, including driving and charging time. Pricing for recharging electric vehicles at public chargers is more complex and diverse than for gas filling stations due to the differences in charging rates and the relatively low competition. This research investigates those differences. Using design science research methodology, a method is presented for determining which charging stops result in the lowest possible charging cost for a given route. The method is demonstrated through experiment with random routes within Sweden. The experimental results show that the average cost savings as compared to the duration-optimal route is 15% and 139 SEK per additional hour of trip time. One possible direction for future work is to improve the performance of the algorithm for use in real-time consumer route planning applications.

Today in most design offices the practice when designing infrastructure is to adopt a trial-and-error based approach. This means that one solution to the problem is evaluated and based on the results the design is altered. This process is then repeated until satisfactory results have been achieved. This is an iterative process and in this project, a software has been developed as a tool for the practicing engineer to use for preliminary design. The software carries out this iterative process and optimizes the solution using algorithms. The software is developed to evaluate road traffic composite bridges according to the Eurocodes. The bridges are made up from two main steel beams with a concrete deck on top. The software has then been used in a number of different studies in order to evaluate its potential as a preliminary design method. The results show that this is a viable tool for preliminary designs, with the drawback that, each iteration is computationally expensive and therefore, optimization of a real-case problem takes a lot of time in order to find the optimal design. / I dagsläget använder de flesta konstruktionsföretagen sig av en iterativ process när de konstruerar infrastruktur. Man utvärderar en lösning och beroende av resultatet så justeras lösningen och utvärderas på nytt, detta återupprepas tills en tillfredsställande lösning har erhållits. Detta är en iterativ process och i samband med detta arbete så har en mjukvara utvecklats som ett verktyg till konstruktören. Mjukvaran utför det iterativa arbetet och optimerar konstruktionen med hjälp av optimeringsalgoritmer. Programmet utvärderar vägbroar av en samverkanskonstruktion i form av två stålbalkar med en betongplatta ovanpå, beräkningarna sker enligt Eurokoderna. Programmet har använts för att utvärdera det här arbetssättets potential vid anbudsskede i projekt. Resultaten visar att det finns potential för detta med nackdelen att varje iteration är tidskrävande vilket gör att optimering av ett realistiskt projekt tar lång tid.

The United Nations’ Sustainable Development Goal 7 (SDG7) of Agenda 2030 calls for an increase in the use of renewable energy sources, among other targets. The percentage of fossil fuel-fired thermal generation for electricity is increasingly being reduced as renewable energy technologies (RET) advance in cost-competitiveness, and as greenhouse gas and industrial air pollutant emission limits become more stringent. In certain cases, renewable energy contributes to energy security by improving a nation’s trade balance, since local resources are harnessed and imports are reduced. RET investments are becoming more frequent gaining a sizeable share in the electric power mix of numerous countries. However, RET is affected by existing fossil fuel-fired electricity generation, especially in countries that have domestic reserves. While coal may be dirty, others such as natural gas provide multiple benefits, presenting a challenge to renewables. Additionally, RET endowment varies for each geographical location. This often does not correspond to the location of major electricity demand centers.  Therefore, large scale RET adoption and integration becomes logistically more cumbersome, as it necessitates existence of a developed grid network. Utilizing a series of analyses in two different settings – Africa and Cyprus – this thesis draws insights on RET growth policy and the level of technology representation in long term energy models. In order to capture specific challenges of RET integration, enhancements in traditional long-term energy system models are called for and carried out.  The case of Africa is used to assess adoption of RET under various trade scenarios. It is home to some of the world’s greatest RET resource potential and the single largest potential RET project, Grand Inga.  While, the island of Cyprus has goals of introducing large percentages of RET into its electric power mix. Each have important idiosyncrasies which are reflected in the analysis. On the one hand, natural gas competes with RET in Cyprus and forms a key transition fuel away from oil. On the other hand, lack of cross-border interconnectors limit RET project development across Africa. / <p>QC 20170519</p>

In this thesis, the optimum design algorithm is presented for reinforced concrete special moment frames. The objective function is considered as the total cost of reinforced concrete frame which includes the cost of concrete, formwork and reinforcing steel bars. The cost of any component is inclusive of material, fabrication and labor. The design variables in beams are selected as the width and the depth of beams in each span, the diameter and the number of longitudinal reinforcement bars along the span and supports. In columns the width and the depth of the column section, the number and the diameter of bars in x and y directions are selected as design variables. The column section database is prepared which includes the width and height of column section, the diameter and the number of reinforcing bars in the column section is constructed. This database is used by the design algorithm to select appropriate sections for the columns of the frame under consideration. The design constraints are implemented from ACI 318-05 which covers the flexural and shear strength, serviceability, the minimum and maximum steel percentage for flexural and shear reinforcement, the spacing requirements for the reinforcing bars and the upper and lower bound requirements for the concrete sections. The optimum design problem formulated according to ACI 318-05 provisions with the design variables mentioned above turns out to be a combinatorial optimization problem. The solution of the design problem is obtained by using the harmony search algorithm (HS) which is one of the recent additions to meta-heuristic optimization techniques which are widely used in obtaining the solution of combinatorial optimization problems. The HS algorithm is quite simple and has few parameters to initialize and consists of simple steps which make it easy to implement. Number of design examples is presented to demonstrate the efficiency and robustness of the optimum design algorithm developed.

The distribution system which forms the final connection between customers and power source plays a vital role in an electrical network. Different studies show that substantial proportion of the customer interruptions occurs due to the failures on distribution network. The ongoing privatization process of the electrical distribution services in Turkey raises the importance of reliable and continuous electricity supply significantly. The new regulations come up with this privatization process and the electrical distribution companies are strictly required to comply with these regulations to ensure the reliability of the distribution network. The legal framework and severe punishments applied to the electrical distribution companies exceeding the continuity of supply indices force them to invest on their network in order to increase the reliability of their system. As the reliability of electricity supplied increases, investment cost also increases. However, low system reliability causes higher outage frequency and duration which will increase the damage of these outages to customers and also increases the cost of the distribution company as a result of the penalty payments. This tradeoff between Outage Cost and Utility Cost requires consideration of an optimization when determining the optimal reliability level. In rural areas where electrical distribution network consists of long radial overhead lines in arborescent structure, continuity of supply is a major problem due to the high failure rates. The implementation of protection devices having reclosing capability and automated sectionalizing switches enhances the continuity of supply on rural networks substantially. The balance between the cost associated with installation of switches and the reduction on Outage Cost is an important optimization issue for distribution network operators. In this thesis study an algorithm is developed in order to determine the optimum number and locations of the sectionalizing switches on a rural electrical distribution network in Turkey which gives an optimum investment level with an optimum Outage Cost.