Economic and Environmental Optimization of Ornamental Stone Block Cutting with a 3D Algorithm

This thesis focuses on finding the optimum block cutting dimensions in terms of the environmental and economic factors by using a 3D algorithm for a limestone quarry in Foggia, Italy. The environmental concerns of quarrying operations are mainly: energy consumption, material waste, and pollution. The main economic concerns are the block recovery, the selling prices, and the production costs. Fractures adversely affect the block recovery ratio. With a fracture model, block production can be optimized. In this research, the waste volume produced by quarrying was minimised to increase the recovery ratio and ensure economic benefits. SlabCutOpt is a software developed at DICAM–University of Bologna for block cutting optimization which tests different cutting angles on the x-y-z planes to offer up alternative cutting methods. The program tests several block sizes and outputs the optimal result for each entry. By using SlabCutOpt, ten different block dimensions were analysed, the results indicated the maximum number of non-intersecting blocks for each dimension. After analysing the outputs, the block named number 1 with the dimensions ‘1mx1mx1m’ had the highest recovery ratio as 43% and the total Relative Money Value (RMV) with a value of 22829. Dimension number 1, also had the lowest waste volume, with a value of 3953.25 m3, for the total bench. For cutting the total bench volume of 6932.25m3, the diamond wire cutter had the lowest dust emission values for the block with the dimension ‘2mx2mx2m’, with a value of 24m3. When compared with the Eco-Label standards, block dimensions having surface area values lower than 15m2, were found to fit the natural resource waste criteria of the label, as the threshold required 25% of minimum recovery [1]. Due to the relativity of production costs, together with the Eco-Label threshold, the research recommends the selection of the blocks with a surface area value between 6m2 and 14m2.

Life cycle assessment (LCA) of a bio-fuel cell fed with waste biomass: potential for scale-up and process optimization

In this Thesis, a life cycle analysis (LCA) of a biofuel cell designed by a team from the University of Bologna was done. The purpose of this study is to investigate the possible environmental impacts of the production and use of the cell and a possible optimization for an industrial scale-up. To do so, a first part of the paper was devoted to studying the present literature on biomass, and fuel cell treatments and then LCA studies on them. The experimental part presents the work done to create the Life Cycle Inventory and Life Cycle Impact Assessment. Several alternative scenarios were created to study process optimization. Reagents and energy supply were changed. To examine whether this technology can be competitive, a comparison was made with some biofuel cell use scenarios with traditional biomass treatment technologies. The result of this study is that this technology is promising from an environmental point of view in case it is possible to recover nutrients in output, without excessive energy consumption, and to minimize the use of energy used to prepare the solution.

Modelling, simulation and optimization of maintenance considerations on condition based maintenance

Globalization has increased the pressure on organizations and companies to operate in the most efficient and economic way. This tendency promotes that companies concentrate more and more on their core businesses, outsource less profitable departments and services to reduce costs. By contrast to earlier times, companies are highly specialized and have a low real net output ratio. For being able to provide the consumers with the right products, those companies have to collaborate with other suppliers and form large supply chains. An effect of large supply chains is the deficiency of high stocks and stockholding costs. This fact has lead to the rapid spread of Just-in-Time logistic concepts aimed minimizing stock by simultaneous high availability of products. Those concurring goals, minimizing stock by simultaneous high product availability, claim for high availability of the production systems in the way that an incoming order can immediately processed. Besides of design aspects and the quality of the production system, maintenance has a strong impact on production system availability. In the last decades, there has been many attempts to create maintenance models for availability optimization. Most of them concentrated on the availability aspect only without incorporating further aspects as logistics and profitability of the overall system. However, production system operator’s main intention is to optimize the profitability of the production system and not the availability of the production system. Thus, classic models, limited to represent and optimize maintenance strategies under the light of availability, fail. A novel approach, incorporating all financial impacting processes of and around a production system, is needed. The proposed model is subdivided into three parts, maintenance module, production module and connection module. This subdivision provides easy maintainability and simple extendability. Within those modules, all aspect of production process are modeled. Main part of the work lies in the extended maintenance and failure module that offers a representation of different maintenance strategies but also incorporates the effect of over-maintaining and failed maintenance (maintenance induced failures). Order release and seizing of the production system are modeled in the production part. Due to computational power limitation, it was not possible to run the simulation and the optimization with the fully developed production model. Thus, the production model was reduced to a black-box without higher degree of details.

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly larger than seen during training.

Dosimetric optimization of CT protocols for the preoperative planning of hip arthroplasty

Radiation dose in x-ray computed tomography (CT) has become a topic of great interest due to the increasing number of CT examinations performed worldwide. In fact, CT scans are responsible of significant doses delivered to the patients, much larger than the doses due to the most common radiographic procedures. This thesis work, carried out at the Laboratory of Medical Technology (LTM) of the Rizzoli Orthopaedic Institute (IOR, Bologna), focuses on two primary objectives: the dosimetric characterization of the tomograph present at the IOR and the optimization of the clinical protocol for hip arthroplasty. In particular, after having verified the reliability of the dose estimates provided by the system, we compared the estimates of the doses delivered to 10 patients undergoing CT examination for the pre-operative planning of hip replacement with the Diagnostic Reference Level (DRL) for an osseous pelvis examination. Out of 10 patients considered, only for 3 of them the doses were lower than the DRL. Therefore, the necessity to optimize the clinical protocol emerged. This optimization was investigated using a human femur from a cadaver. Quantitative analysis and comparison of 3D reconstructions were made, after having performed manual segmentation of the femur from different CT acquisitions. Dosimetric simulations of the CT acquisitions on the femur were also made and associated to the accuracy of the 3D reconstructions, to analyse the optimal combination of CT acquisition parameters. The study showed that protocol optimization both in terms of Hausdorff distance and in terms of effective dose (ED) to the patient may be realized simply by modifying the value of the pitch in the protocol, by choosing between 0.98 and 1.37.

Nuovo modello cinetostatico della caviglia umana.

The relevance of human joint models has been shown in the literature. They can help in diagnosis, in prostheses and ortheses design and in predicting the joints’ behavior. Recently a sequential approach for the modeling of the human diarthrodial joints composed of three steps has been proposed. At each step the role of some anatomical structures is considered. Starting from a limited number of structures, the model gets more and more sophisticated until all the components, both passive (articular surfaces, ligaments and tendons) and active (muscles), are incorporated in the final model. According to this procedure, the behavior of the human ankle during passive motion (no loads applied) has been previously modeled by a one degree of freedom 5-5 fully parallel mechanism. Starting from this model, the kinetostatic model of the human ankle joint that replicates its behavior when external loads are applied is developed. The anatomical and mechanical characteristics and the role of the passive structures are considered; a multifiber model is developed and an optimization criteria based on experimental data is proposed. Finally an application of the developed model to an amputated ankle is presented, together with the results obtained from the optimization of the geometrical and mechanical Parameters. Although some improvements can be achieved, the model is satisfactorily able to replicate the behavior of the human ankle subject to the anterior drawer and the inversion clinical tests applied in the neutral position.

Studio fluidodinamico di un reattore continuo agitato meccanicamente tramite tecniche PIV e PLIF

The study and understanding of the motion of the fluid phases in a mechanically stirred reactor has always been, and still are, an open problem which absorbs the study and the work of many researchers. In recent decades, thanks to the growing opportunities offered by the development of technology, we have made great strides in the understanding of mixing, one of the major unit operations at the base of many industrial processes. A complete understanding of this process and its optimization for industrial applications is a challenging task due to the complex interactions between the many factors at play that include physical, chemical and biological. The purpose of this thesis is the study of a fluid-mechanically-agitated continuous reactor through the use of optical diagnostic techniques, which allowed to determine the range of motion and the time of perfect homogenization in a reactor of standard geometry in different operating conditions.

Development of Optimal Energy Management Strategies for a Hybrid Boat

The present work proposes different approaches to extend the mathematical methods of supervisory energy management used in terrestrial environments to the maritime sector, that diverges in constraints, variables and disturbances. The aim is to find the optimal real-time solution that includes the minimization of a defined track time, while maintaining the classical energetic approach. Starting from analyzing and modelling the powertrain and boat dynamics, the energy economy problem formulation is done, following the mathematical principles behind the optimal control theory. Then, an adaptation aimed in finding a winning strategy for the Monaco Energy Boat Challenge endurance trial is performed via ECMS and A-ECMS control strategies, which lead to a more accurate knowledge of energy sources and boat’s behaviour. The simulations show that the algorithm accomplishes fuel economy and time optimization targets, but the latter adds huge tuning and calculation complexity. In order to assess a practical implementation on real hardware, the knowledge of the previous approaches has been translated into a rule-based algorithm, that let it be run on an embedded CPU. Finally, the algorithm has been tuned and tested in a real-world race scenario, showing promising results.

Design and optimization of turbo-expanders for organic rankine cycles

In a world focused on the need to produce energy for a growing population, while reducing atmospheric emissions of carbon dioxide, organic Rankine cycles represent a solution to fulfil this goal. This study focuses on the design and optimization of axial-flow turbines for organic Rankine cycles. From the turbine designer point of view, most of this fluids exhibit some peculiar characteristics, such as small enthalpy drop, low speed of sound, large expansion ratio. A computational model for the prediction of axial-flow turbine performance is developed and validated against experimental data. The model allows to calculate turbine performance within a range of accuracy of ±3%. The design procedure is coupled with an optimization process, performed using a genetic algorithm where the turbine total-to-static efficiency represents the objective function. The computational model is integrated in a wider analysis of thermodynamic cycle units, by providing the turbine optimal design. First, the calculation routine is applied in the context of the Draugen offshore platform, where three heat recovery systems are compared. The turbine performance is investigated for three competing bottoming cycles: organic Rankine cycle (operating cyclopentane), steam Rankine cycle and air bottoming cycle. Findings indicate the air turbine as the most efficient solution (total-to-static efficiency = 0.89), while the cyclopentane turbine results as the most flexible and compact technology (2.45 ton/MW and 0.63 m3/MW). Furthermore, the study shows that, for organic and steam Rankine cycles, the optimal design configurations for the expanders do not coincide with those of the thermodynamic cycles. This suggests the possibility to obtain a more accurate analysis by including the computational model in the simulations of the thermodynamic cycles. Afterwards, the performance analysis is carried out by comparing three organic fluids: cyclopentane, MDM and R245fa. Results suggest MDM as the most effective fluid from the turbine performance viewpoint (total-to-total efficiency = 0.89). On the other hand, cyclopentane guarantees a greater net power output of the organic Rankine cycle (P = 5.35 MW), while R245fa represents the most compact solution (1.63 ton/MW and 0.20 m3/MW). Finally, the influence of the composition of an isopentane/isobutane mixture on both the thermodynamic cycle performance and the expander isentropic efficiency is investigated. Findings show how the mixture composition affects the turbine efficiency and so the cycle performance. Moreover, the analysis demonstrates that the use of binary mixtures leads to an enhancement of the thermodynamic cycle performance.

Application and optimization of a thermal energy storage on a cruise ship

The thesis, developed in collaboration between the team Systems and Equipment for Energy and Environment of Bologna University and Chalmers University of Technology in Goteborg, aims to study the benefits resulting from the adoption of a thermal storage system for marine application. To that purpose a chruis ship has been considered. To reach the purpose has been used the software EGO (Energy Greed Optimization) developed by University of Bologna.

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

Development and optimization of fibre-shaped dye-sensitized solar cells employing an innovative fully organic sensitizer

The quality of human life depends to a large degree on the availability of energy. In recent years, photovoltaic technology has been growing extraordinarily as a suitable source of energy, as a consequence of the increasing concern over the impact of fossil fuels on climate change. Developing affordable and highly efficiently photovoltaic technologies is the ultimate goal in this direction. Dye-sensitized solar cells (DSSCs) offer an efficient and easily implementing technology for future energy supply. Compared to conventional silicon solar cells, they provide comparable power conversion efficiency at low material and manufacturing costs. In addition, DSSCs are able to harvest low-intensity light in diffuse illumination conditions and then represent one of the most promising alternatives to the traditional photovoltaic technology, even more when trying to move towards flexible and transparent portable devices. Among these, considering the increasing demand of modern electronics for small, portable and wearable integrated optoelectronic devices, Fibre Dye-Sensitized Solar Cells (FDSSCs) have gained increasing interest as suitable energy provision systems for the development of the next-generation of smart products, namely “electronic textiles” or “e-textiles”. In this thesis, several key parameters towards the optimization of FDSSCs based on inexpensive and abundant TiO2 as photoanode and a new innovative fully organic sensitizer were studied. In particular, the effect of various FDSSCs components on the device properties pertaining to the cell architecture in terms of photoanode oxide layer thickness, electrolytic system, cell length and electrodes substrates were examined. The photovoltaic performances of the as obtained FDSSCs were fully characterized. Finally, the metal part of the devices (wire substrate) was substituted with substrates suitable for the textile industry as a fundamental step towards commercial exploitation.

Crowd-shipping and autonomous vehicles: an optimization model for the last mile delivery

Il seguente elaborato propone un modello innovativo per la gestione della logistica distributiva nell’ultimo miglio, congiungendo l’attività di crowd-shipping con la presenza di Autonomous Vehicles, per il trasporto di prodotti all’interno della città. Il crowd-shipping utilizza conducenti occasionali, i quali deviano il loro tragitto in cambio di una ricompensa per il completamento dell’attività. Dall’altro lato, gli Autonomous Vehicles sono veicoli elettrici a guida autonoma, in grado di trasportare un numero limitato di pacchi e dotati di un sistema di sicurezza avanzato per garantire la fiducia nel trasporto. In primo luogo, nel seguente elaborato verrà mostrato il modello di ottimizzazione che congiunge i due attori principali in un unico ambiente, dove sono presenti un numero determinato di prodotti da muovere. Successivamente, poiché il problema di ottimizzazione è molto complesso e il numero di istanze valutabili è molto basso, verranno presentate due soluzioni differenti. La prima riguarda la metaeuristica chiamata Ant System, che cerca di avvicinarsi alle soluzioni ottime del precedente modello, mentre la seconda riguarda l’utilizzo di operatori di Local Search, i quali permettono di valutare soluzioni per istanze molto più grandi rispetto alla metaeuristica. Infine, i due modelli euristici verranno utilizzati per analizzare uno scenario che cerca di riprodurre una situazione reale. Tale scenario tenta di allocare strategicamente le risorse presenti e permette di dimostrare che gli Autonomous Vehicles riescono a supportare gli Occasional Drivers anche quando il numero di prodotti trasportabili è elevato. Inoltre, le due entità proposte riescono a soddisfare la domanda, garantendo un servizio che nel futuro potrebbe sostituire il tradizionale sistema di logistica distributiva last mile.

Optimization and characterization of a wheat-based biocomposite for additive manufacturing

In recent years plastic is a key material, fundamental for a large variety of implications. But its large utilization has the direct consequence of the disposal of tons of wastes, that in the major amount cannot be recycled. To pose a solution to the issue of the increasing amount of plastics that are rapidly accumulating all over the world, it is necessary to switch to biodegradable materials. This dissertation is focused on a biodegradable and biobased plastic (polylactic acid, PLA), in order to set free the material from the market and issues of oil, but the PLA is very expensive. For this reason it was added a part of natural component derived from wastes (wheat middling), that acts as reinforcement and decreases the cost of the final material forming a biocomposite. Since their interaction is difficult, the purpose of this dissertation is to improve the affinity between the PLA and the wheat middling.