Throughout a very long piping network crude oil in Saudi Arabia is sent to Gas Oil Separation Plant called GOSP. The main objectives of the GOSP are: - Separation of the associated gas through pressure drop in two series stages one to 120 psig and the other to 50 psig. - Separation of water by gravity separators called High Pressure Production Trap (HPPT), Dehydrator, Desalter and Water Oil Separator (WOSEP). - Reducing salt concentration to less than 10 PTB utilizing wash water and demulsifier. During the desalting process, the challenge is to overcome the existence of an emulsion layer at the interface between oil and water. In petroleum industry normally emulsions encountered are some kind of water droplets dispersed in a continuous phase of oil. In crude oil emulsions, emulsifying agents are present at the oil-water interface, hindering this coalescence process. Such agents include scale and clay particles, added chemicals or indigenous crude oil components like asphaltenes, resins, waxes and naphthenic acids. Many techniques made available to gas oil separation plant operators to minimize the effect of tight emulsions. These techniques include injection of demulsifier, increasing oil temperature, gravity separation in large vessels with high retention time as well as electrostatic voltage. From experience and studies these variables have been already optimized to a good extent; however, from the believe that knowledge never stop, this study is conducted targeting enhancing the demulsifier control and optimizing the wash water rate. The objective of this study is to design an Artificial Neural Network (ANN) trained on data set to cover wide operating range of all parameters effecting demulsifier dosage. This network will be used to work as a control black box inside the controller in which all effecting parameters are inputs and the demulsifier dosage is the controller output. Testing this control scheme showed an effective reduction in demulsifier consumption rate compared to the existing linear method. Results also, showed that the existing control strategy is highly conservative to prevent the salt from exceeding the limit. The generated function from the ANN was used also to optimize the amount of fresh water added to wash the salty crude oil. Finally, another ANN was developed to generate an online estimate of the salt content in the produced oil.

Throughout a very long piping network crude oil in Saudi Arabia is sent to Gas Oil Separation Plant called GOSP. The main objectives of the GOSP are: - Separation of the associated gas through pressure drop in two series stages one to 120 psig and the other to 50 psig. - Separation of water by gravity separators called High Pressure Production Trap (HPPT), Dehydrator, Desalter and Water Oil Separator (WOSEP). - Reducing salt concentration to less than 10 PTB utilizing wash water and demulsifier. During the desalting process, the challenge is to overcome the existence of an emulsion layer at the interface between oil and water. In petroleum industry normally emulsions encountered are some kind of water droplets dispersed in a continuous phase of oil. In crude oil emulsions, emulsifying agents are present at the oil-water interface, hindering this coalescence process. Such agents include scale and clay particles, added chemicals or indigenous crude oil components like asphaltenes, resins, waxes and naphthenic acids. Many techniques made available to gas oil separation plant operators to minimize the effect of tight emulsions. These techniques include injection of demulsifier, increasing oil temperature, gravity separation in large vessels with high retention time as well as electrostatic voltage. From experience and studies these variables have been already optimized to a good extent; however, from the believe that knowledge never stop, this study is conducted targeting enhancing the demulsifier control and optimizing the wash water rate. The objective of this study is to design an Artificial Neural Network (ANN) trained on data set to cover wide operating range of all parameters effecting demulsifier dosage. This network will be used to work as a control black box inside the controller in which all effecting parameters are inputs and the demulsifier dosage is the controller output. Testing this control scheme showed an effective reduction in demulsifier consumption rate compared to the existing linear method. Results also, showed that the existing control strategy is highly conservative to prevent the salt from exceeding the limit. The generated function from the ANN was used also to optimize the amount of fresh water added to wash the salty crude oil. Finally, another ANN was developed to generate an online estimate of the salt content in the produced oil.

The U.N. projects the world population to reach nearly 10 billion people by 2050, which will cause demand for manufactured goods to reach unforeseen levels. In order for us to produce the goods to support an equitable future, the methods in which we manufacture those goods must radically change. The emerging Circular Economy (CE) concept for production systems has promised to drastically increase economic/business value by significantly reducing the world’s resource consumption and negative environmental impacts. However, CE is inherently limited because of its emphasis on recycling and reuse of materials. CE does not address the holistic changes needed across all of the fundamental elements of manufacturing: products, processes, and systems. Therefore, a paradigm shift is required for moving from sustainment to sustainability to “produce more with less” through smart, innovative and transformative convergent manufacturing approaches rooted in redesigning next generation manufacturing infrastructure. This PhD research proposes the Helical Economy (HE) concept as a novel extension to CE. The proposed HE concepts shift the CE’s status quo paradigm away from post-use recovery for recycling and reuse and towards redesigning manufacturing infrastructure at product, process, and system levels, while leveraging IoT-enabled data infrastructures and an upskilled workforce. This research starts with the conceptual overview and a framework for implementing HE in the discrete product manufacturing domain by establishing the future state vision of the Helical Economy Manufacturing Method (HEMM). The work then analyzes two components of the framework in detail: designing next-generation products and next-generation IoT-enabled data infrastructures. The major research problems that need to be solved in these subcomponents are identified in order to make near-term progress towards the HEMM. The work then proceeds with the development and discussion of initial methods for addressing these challenges. Each method is demonstrated using an illustrative industry example. Collectively, this initial work establishes the foundational body of knowledge for the HE and the HEMM, provides implementation methods at the product and IoT-enabled data infrastructure levels, and it shows a great potential for HE’s ability to create and maximize sustainable value, optimize resource consumption, and ensure continued technological progress with significant economic growth and innovation. This research work then presents an outlook on the future work needed, as well as calls for industry to support the continued refinement and development of the HEMM through relevant prototype development and subsequent applications.

Simulated Moving Bed (SMB) chromatography is a separation process where the components are separated due to their varying affinity towards the stationary phase. Over the past decade, many modifications have been proposed in SMB chromatography in order to effectively separate a binary mixture. However, the separation of multi-component mixtures using SMB is still one of the major challenges. Although many different strategies have been proposed, previous studies have rarely performed comprehensive investigations for finding the best ternary separation strategy from various possible alternatives. Furthermore, the concept of combining reaction with SMB has been proposed in the past for driving the equilibrium limited reactions to completion by separating the products from the reaction zone. However, the design of such systems is still challenging due to the complex dynamics of simultaneous reaction and adsorption. The first objective of the study is to find the best ternary separation strategy among various alternatives design of SMB. The performance of several ternary SMB operating schemes, that are proposed in the literature, are compared in terms of the optimal productivity obtained and the amount of solvent consumed. A multi- objective optimization problem is formulated which maximizes the SMB productivity and purity of intermediate eluting component at the same time. Furthermore, the concept of optimizing a superstructure formulation is proposed, where numerous SMB operating schemes can be incorporated into a single formulation. This superstructure approach has a potential to find more advantageous operating scheme compared to existing operating schemes in the literature. The second objective of the study is to demonstrate the Generalized Full Cycle (GFC) operation experimentally for the first time, and compare its performance to the JO process. A Semba OctaveTM chromatography system is used as an experimental SMB unit to implement the optimal operating schemes. In addition, a simultaneous optimization and model correction (SOMC) scheme is used to resolve the model mismatch in a systematic way. We also show a systematic comparison of both JO and GFC operations by presenting a Pareto plot of the productivity achieved against the desired purity of the intermediate eluting component experimentally. The third objective of the study is to develop an simulated moving bed reactor (SMBR) process for an industrial-scale application, and demonstrate the potential of the ModiCon operation for improving the performance of the SMBR compared to the conventional operating strategy. A novel industrial application involving the esterification of acetic acid and 1-methoxy-2-propanol is considered to produce propylene glycol methyl ether (PMA) as the product. A multi-objective optimization study is presented to find the best reactive separation strategy for the production of the PMA product. We also present a Pareto plot that compares the ModiCon operation, which allows periodical change of the feed composition and the conventional operating strategy for the optimal production rate of PMA that can be achieved against the desired conversion of acetic acid.

This diploma thesis deals with the optimization of the procurement process at ŠKODA AUTO a.s. Specifically with Forward Sourcing, which is responsible for the nomination of suppliers for new parts of starting projects. Optimization will be done within a benchmark analogical to identical process in Volkswagen AG. The aim is to create proposals for changes and recommendations for an effective functioning of Forward Sourcing. The theoretical part will be defined in terms of process management and procurement within the organization. The practical part focuses on the analysis, comparison and measurement process from which proposals for changes and recommendations will be determined.

Massive parallelism combined with complex memory hierarchies and heterogeneity in high-performance computing (HPC) systems form a barrier to efficient application and architecture design. The performance achievements of the past must continue over the next decade to address the needs of scientific simulations. However, building an exascale system by 2022 that uses less than 20 megawatts will require significant innovations in power and performance efficiency. A key limitation of past approaches is a lack of power-performance policies allowing users to quantitatively bound the effects of power management on the performance of their applications and systems. Existing controllers and predictors use policies fixed by a knowledgeable user to opportunistically save energy and minimize performance impact. While the qualitative effects are often good and the aggressiveness of a controller can be tuned to try to save more or less energy, the quantitative effects of tuning and setting opportunistic policies on performance and power are unknown. In other words, the controller will save energy and minimize performance loss in many cases but we have little understanding of the quantitative effects of controller tuning. This makes setting power-performance policies a manual trial and error process for domain experts and a black art for practitioners. To improve upon past approaches to high-performance power management, we need to quantitatively understand the effects of power and performance at scale. In this work, I have developed theories and techniques to quantitatively understand the relationship between power and performance for high performance systems at scale. For instance, our system-level, iso-energy-efficiency model analyzes, evaluates and predicts the performance and energy use of data intensive parallel applications on multi-core systems. This model allows users to study the effects of machine and application dependent characteristics on system energy efficiency. Furthermore, this model helps users isolate root causes of energy or performance inefficiencies and develop strategies for scaling systems to maintain or improve efficiency.  I have also developed methodologies which can be extended and applied to model modern heterogeneous architectures such as GPU-based clusters to improve their efficiency at scale. / Ph. D.

Aevitas is an industrial wastewater treatment plant that receives about 300 m3/day of mixture of wastewater from different industries. The chemical oxygen demand of higher 600 ppm and the variety of the chemical constitution of industrial wastewater are two significant problems on Aevitas. Therefore, there is a strong need for developing advanced analytical techniques that can identify the specific compounds that are the source of COD. During 10 months, about 75 industrial samples were characterized using a battery of tests including GC/MS, COD, TOC, and pH to identify the chemicals that are main source of COD in the industrial wastewaters. Results showed that the COD of 87% of 75 provided samples from Aevitas plant was higher than 600. At the first step of process design, activated carbon was used to eliminate the identified organic chemicals from the wastewaters. The maximum and minimum of COD removal (depends on the chemical composition) of the wastewaters were obtained as 94 and 24%, respectively. Moreover, the amount of COD and TOC that can be adsorbed on the surface of 1 gram of the activated carbon were 25 and 7 mg, respectively. Although activated carbon is capable to reduce the COD, its capacity of adsorption is limited. To overcome this problem an alternative process, membrane filtration was applied for COD removal. Two types of crossflow NF (NF270, NF90, NFX, NFW, NFS, TS80, XN45, and SXN2_L) and RO (BW60 and TW30) membranes in two modules of the spiral wound and flat sheet were used. The filtration results of 11 different industrial wastewaters showed that NF90, TS80, NFX, and NFS were effective in COD removal. However, in terms of output flux NFX and NFS flat sheet were better than others were. Similar to the activated carbon process, the COD removal in filtration process was between 30 and 90%. The obtained results can be used to scale up the membrane filtration process at Aevitas. / Thesis / Master of Chemical Engineering (MChE) / Aevitas is an industrial wastewater treatment plant, which is situated at the City of Brantford. Every day, this plant receives about 15 trucks of the mixture of wastewaters from many different industries. The input wastewater into the plant should be treated and meet the environmental standard so that it can be discharged into a municipal wastewater plant. Currently, the maximum allowable chemical oxygen demand (COD) for discharging the treated wastewater from Aevitas to the municipal wastewater treatment plant is 600 ppm. Despite the fact, the current system in Aevitas is not efficient to meet this criterion. Thus, we strive to design efficient processes to overcome the problem. To this end, 75 samples were collected from Aevitas to observe the kind of chemicals that are the source of COD and then, two processes including activated carbon adsorption and membrane filtration were used for further reduction of COD. Although activated carbon can reduce the COD, the limited adsorption capacity was a major concern for its long-term application, especially if the COD of influent wastewater is higher than 2000 ppm. Membrane filtration was used as an alternative for activated carbon and the results showed that membrane could reduce the COD below 600 in 48% of the cases.

The radio spectrum that can be used for wireless communications is a finite but extremely valuable resource. During the past two decades, with the proliferation of new wireless applications, the use of the radio spectrum has intensified to the point that improved spectrum sharing policies and new mechanisms are needed to enhance its utilization efficiency. This dissertation studies spectrum sharing and coexistence on both licensed and unlicensed bands for wireless networks. For licensed bands, we study two coexistence paradigms: transparent coexistence (a.k.a., underlay) and policy-based network cooperation (a.k.a., overlay). These two paradigms can offer significant improvement in spectrum utilization and throughput performance than the interweave paradigm. For unlicensed band, we study coexistence of Wi-Fi and LTE, the two most poplar wireless networks. / Ph. D.

Interference is a fundamental problem in wireless networks. An effective solution to this problem usually calls for a cross-layer approach. Although there exist a large volume of works on interference management techniques in the literature, most of them are limited to signal processing at the physical (PHY) layer or information-theoretic exploitation. Studies of advanced interference techniques from a cross-layer optimization perspective remain limited, especially involving multi-hop wireless networks. This dissertation aims at filling this gap by offering a comprehensive investigation of three interference techniques: interference cancellation (IC), interference alignment (IA), and interference neutralization (IN). This dissertation consists of three parts: the first part studies IC in distributed multi-hop multiple-input multiple-output (MIMO) networks; the second part studies IA in multi-hop networks, cellular networks, and underwater acoustic (UWA) networks; and the third part focuses on IN in multi-hop single-antenna networks. While each part makes a step towards advancing an interference technique, they collectively constitute a body of work on interference management in the networking research community. Results in this dissertation not only advance network-level understanding of the three interference management techniques, but also offer insights and guidance on how these techniques may be incorporated in upper-layer protocol design. In the first part, we study IC in multi-hop MIMO networks where resource allocation is achieved through neighboring node coordination and local information exchange. Based on a well-established degree-of-freedom (DoF) MIMO model, we develop a distributed DoF scheduling algorithm with the objective of maximizing network-level throughput while guaranteeing solution feasibility at the PHY layer. The proposed algorithm accomplishes a number of beneficial features, including polynomial-time complexity, amenability to local implementation, a guarantee of feasibility at the PHY layer, and competitive throughput performance. Our results offer a definitive ``yes'' answer to the question --- Can the node-ordering DoF model be deployed in a distributed multi-hop MIMO network? In particular, we show that the essence of the DoF model --- a global node ordering, can be implicitly achieved via local operations, albeit it is invisible to individual node. In the second part, we investigate IA in various complex wireless networks from a networking perspective. Specifically, we study IA in three different domains: spatial domain, spectral domain, and temporal domain. In the spatial domain, we study IA for multi-hop MIMO networks. We derive a set of simple constraints to characterize the IA capability at the PHY layer. We prove that as long as the set of simple constraints are satisfied, there exists a feasible IA scheme (i.e., precoding and decoding vectors) at the PHY layer so that the data streams on each link can be transported free of interference. Therefore, instead of dealing with the complex design of precoding and decoding vectors, our IA constraints only require simple algebraic addition/subtraction operations. Such simplicity allows us to study network-level IA problems without being distracted by the tedious details in signal design at the PHY layer. Based on these IA constraints, we develop an optimization framework for unicast and multicast communications. In the spectral domain, we study IA in OFDM-based cellular networks. Different from spatial IA, spectral IA is achieved by mapping data streams onto a set of frequency bands/subcarriers (rather than a set of antenna elements). For the uplink, we derive a set of simple IA constraints to characterize a feasible DoF region for a cellular network. We show how to construct precoding and decoding vectors at the PHY layer so that each data stream can be transported free of interference. Based on the set of IA constraints, we study a user throughput maximization problem and show the throughput improvement over two other schemes via numerical results. For the downlink, we find that we can exploit the uplink IA constraints to the downlink case simply by reversing the roles of user and base station. Further, the downlink user throughput maximization problem has the exactly same formulation as the uplink problem and thus can be solved in the exactly same way. In the temporal domain, we study IA for UWA networks. A fundamental issue in UWA networks is large propagation delays due to slow signal speed in water medium. But temporal IA has the potential to turn the adverse effect of large propagation delays into something beneficial. We propose a temporal IA scheme based on propagation delays, nicknamed PD-IA, for multi-hop UWA networks. We first derive a set of PD-IA constraints to guarantee PD-IA feasibility at the PHY layer. Then we develop a distributed PD-IA scheduling algorithm, called Shark-IA, to maximally overlap interference in a multi-hop UWA network. We show that PD-IA can turn the adverse propagation delays to throughput improvement in multi-hop UWA networks. In the third part, we study IN for multi-hop single-antenna networks with full cooperation among the nodes. The fundamental problem here is node selection for IN in a multi-hop network environment. We first establish an IN reference model to characterize the IN capability at the PHY layer. Based on this reference model, we develop a set of constraints that can be used to quickly determine whether a subset of links can be active simultaneously. By identifying each eligible neutralization node as a neut, we study IN in a multi-hop network with a set of sessions and derive the necessary constraints to characterize neut selection, IN, and scheduling. These constraints allow us to study IN problems from a networking perspective but without the need of getting into signal design issues at the PHY layer. By applying our IN model and constraints to study a throughput maximization problem, we show that the use of IN can generally increase network throughput. In particular, throughput gain is most significant when there is a sufficient number of neuts that can be used for IN. In summary, this dissertation offers a comprehensive investigation of three interference management techniques (IC, IA, and IN) from a networking perspective. Theoretical and algorithmic contributions of this dissertation encompass characterization of interference exploitation capabilities at the PHY layer, derivation of tractable interference models, development of feasibility proof for each interference model, formulation of throughput maximization problems, design of distributed IC and PD-IA scheduling algorithms, and development of near-optimal solutions with a performance guarantee. The results in this dissertation offer network-level understanding of the three interference management techniques and lay the groundwork for future research on interference management in wireless networks. / Ph. D.

Over the past decade, wireless data communication has experienced a phenomenal growth, which is driven by the popularity of wireless devices and the growing number of bandwidth hungry applications. During the same period, various advanced communication technologies have emerged to improve network throughput. Some examples include multi-input multi-output (MIMO), full duplex, cognitive radio, mmWave, among others. An important research direction is to understand the impacts of these new technologies on network throughput performance. Such investigation is critical not only for theoretical understanding, but also can be used as a guideline to design algorithms and network protocols in the field. The goal of this dissertation is to understand the impact of some advanced technologies on network throughput performance. More specifically, we investigate the following three technologies: MIMO, full duplex, and mmWave communication. For each technology, we explore the performance envelope of wireless networks by studying a throughput maximization problem. / Ph. D. / As everyone knows, we are now living in a connected world, where network access is available anytime and anywhere. According to Cisco’s report [97], global Internet traffic is expected to reach 2.3 zettabytes per year by 2020, and wireless data traffic will account for 65% of the total Internet traffic. There are three primary contributors for the explosive growth of wireless data demand: the rising number of wireless devices, the increasing number of new applications, and the evergrowing amount of video traffic. Each year, all kinds of smart devices with increased intelligence are introduced in market. The number of wireless devices is predicted to reach 11.6 billion by 2020 [97]. The smart devices enable people to enjoy mobile applications for entertainment, such as social networking, video streaming, and gaming. Such bandwidth hungry applications have changed the wireless data consumption pattern. According to Ericssons report [98], video traffic dominates the mobile data consumption for all kinds of mobile devices. Moreover, the amount of video traffic is still growing more than 50 % annually. To meet the ever-growing traffic demand, innovative technologies have been developed to expand the capacity of wireless networks. Some examples include multi-input multi-output (MIMO), full duplex, cognitive radio, mmWave, ultra-wideband, among others. In this dissertation, we aim to investigate the impact of such advanced technologies on network throughput performance. Such theoretical study is critical since it can be used as a guidline to design real-world network protocols.