Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient prob-lems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intel-ligent fault classification of a transformer. The Multilayer SVM technique is used to de-termine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussi-an functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature, and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy

ESTIMATION OF GREENHOUSE GAS AND ODOUR EMISSIONS FROM COLD REGION MUNICIPAL BIOLOGICAL NUTRIENT REMOVAL WASTEWATER TREATMENT PROCESSES

Rising human populations and ever-increasing demand for potable water result in increased municipal wastewater production. The collection, treatment, and management of municipal wastewaters include energy-intensive processes leading to the generation and emission of greenhouse, potentially toxic, and odorous gases. The main goal of this thesis was to advance knowledge of greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and smelly compound (including ammonia, NH3; and hydrogen sulphide, H2S) emissions from typical municipal wastewater treatment plants (MWTPs) to accurately describe their emission rate estimates (EREs) using operating parameters. This research included laboratory and field assessments of greenhouse gas (GHG) and odour emissions in conjunction with monitored operating parameters. Laboratory-scale reactors simulating open-to-air treatment processes including primary and secondary clarifiers and anaerobic, anoxic, and aerobic reactors, were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes in winter and summer seasons. The Saskatoon Wastewater Treatment plan (SWTP) is a state-of-the-art biological nutrient removal (BNR) type MWTP and a Class IV treatment facility in Canada which was selected as a case study given its highly variable seasonal temperatures from −40 °C to 30 °C and its geographic location near the University of Saskatchewan. The experimental results were then used to develop a variety of novel machine learning models describing gas EREs with further optimization of operating parameters using genetic algorithm (GA). Studied machine learning models were artificial data generation algorithms (including generative adversarial network, GAN) and data-driven models (including artificial neural network, ANN; adaptive network-based fuzzy inference systems, ANFIS; and linear/non-linear regression models). To my knowledge, this is the first application of GAN used for MWTP modelling purposes. Results indicated that anaerobic digestion EREs averagely reached 4,443 kg CH4/d, 9,145 kg CO2/d, and 59.7 kg H2S/d. In contrast, GHG and odour ERE variabilities given ambient temperature changes were more noticeable for open-to-air treatment processes such that the winter EREs were 45,129 kg CO2/d, 21.9 kg CH4/d, 3.20 kg N2O/d, and insignificant for H2S and NH3. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively, and still insignificant NH3 emissions. However, the CO2 EREs were reduced to 37,794 kg CO2/d, and interestingly, NH3 emissions were still negligible. Overall, the aerobic reactor was the dominant source of GHG emissions for both seasons, and changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs. The integration of field monitoring data with data-driven models showed that the ANN, ANFIS, and regression models provided reasonable EREs using: (1) volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate for anaerobic digestion biogas generations; and (2) hydraulic retention time, temperature, total organic carbon, dissolved oxygen, phosphate, and nitrogen concentrations for aerobic GHG emissions. However, when both model accuracy and uncertainty were considered there appears to be a compromise between these parameters with no model having simultaneously both high accuracy and low uncertainty. Additionally, and interestingly, virtual data augmentation using GAN was found to be a valuable resource in supplementation of limited data for improved modelling outcomes. GA was also coupled with the data-driven models to determine optimal operating parameters resulting in either GHG emission maximization given biogas could be beneficial for energy generation or GHG emission minimization given the aerobic reactor is an open-to-air process that can impact nearby residential neighbourhood air quality. The current study provides a hybrid methodology of mathematical modelling and experiments that can be used to accurately estimate and optimize the GHG and odour EREs from other MWTPs in Canada and worldwide

Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions

© 2020 Elsevier Ltd Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R2), Adjusted-R2, Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R2 of 0.9983 and 0.9991 respectively, compared with MLR models with R2 of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33

Agroecological Approaches for Soil Health and Water Management

In the last century, innovations in agricultural technologies centered on maximizing food production to feed the growing population have contributed to significant changes in agroecosystem processes, including carbon, nutrients, and water cycling. There are growing concerns regarding soil fertility depletion, soil carbon loss, greenhouse gas emissions, irrigational water scarcity, and water pollution, affecting soil health, agricultural productivity, systems sustainability, and environmental quality. Soils provide the foundation for food production, soil water and nutrient cycling, and soil biological activities. Therefore, an improved understanding of biochemical pathways of soil organic matter and nutrient cycling, microbial community involved in regulating soil health, and soil processes associated with water flow and retention in soil profile helps design better agricultural systems and ultimately support plant growth and productivity. This book, Agroecological Approaches in Soil and Water Management, presents a collection of original research and review papers studying physical, chemical, and biological processes in soils and discusses multiple ecosystem services, including carbon sequestration, nutrients and water cycling, greenhouse gas emissions, and agro-environmental sustainability. We covered tillage, nutrients, irrigation, amendments, crop rotations, crop residue management practices for improving soil health, soil C and nutrient cycling, greenhouse gas emissions, soil water dynamics, and hydrological processes

A survey on the development status and application prospects of knowledge graph in smart grids

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio

Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.Zlepšení průmyslových procesů, Model založený na datech, Optimalizace procesu, Strojové učení, Průmyslové systémy, Energeticky náročná průmyslová odvětví, Umělá inteligence.

Development of a sustainable groundwater management strategy and sequential compliance monitoring to control saltwater intrusion in coastal aquifers

The coastal areas of the world are characterized by high population densities, an abundance of food, and increased economic activities. These increasing human settlements, subsequent increases in agricultural developments and economic activities demand an increasing amount quantity of freshwater supplies to different sectors. Groundwater in coastal aquifers is one of the most important sources of freshwater supplies. Over exploitation of this coastal groundwater resource results in seawater intrusion and subsequent deterioration of groundwater quality in coastal aquifers. In addition, climate change induced sea level rise, in combination with the effect of excessive groundwater extraction, can accelerate the seawater intrusion. Adequate supply of good quality water to different sectors in coastal areas can be ensured by adoption of a proper management strategy for groundwater extraction. Optimal use of the coastal groundwater resource is one of the best management options, which can be achieved by employing a properly developed optimal groundwater extraction strategy. Coupled simulation-optimization (S-O) approaches are essential tools to obtain the optimal groundwater extraction patterns. This study proposes approaches for developing multiple objective management of coastal aquifers with the aid of barrier extraction wells as hydraulic control measure of saltwater intrusion in multilayered coastal aquifer systems. Therefore, two conflicting objectives of management policy are considered in this research, i.e. maximizing total groundwater extraction for advantageous purposes, and minimizing the total amount of water abstraction from barrier extraction wells. The study also proposes an adaptive management strategy for coastal aquifers by developing a three-dimensional (3-D) monitoring network design. The performance of the proposed methodologies is evaluated by using both an illustrative multilayered coastal aquifer system and a real life coastal aquifer study area. Coupled S-O approach is used as the basic tool to develop a saltwater intrusion management model to obtain the optimal groundwater extraction rates from a combination of feasible solutions on the Pareto optimal front. Simulation of saltwater intrusion processes requires solution of density dependent coupled flow and solute transport numerical simulation models that are computationally intensive. Therefore, computational efficiency in the coupled S-O approach is achieved by using an approximate emulator of the accompanying physical processes of coastal aquifers. These emulators, often known as surrogate models or meta-models, can replace the computationally intensive numerical simulation model in a coupled S-O approach for achieving computational efficiency. A number of meta-models have been developed and compared in this study for integration with the optimization algorithm in order to develop saltwater intrusion management model. Fuzzy Inference System (FIS), Adaptive Neuro Fuzzy Inference System (ANFIS), Multivariate Adaptive Regression Spline (MARS), and Gaussian Process Regression (GPR) based meta-models are developed in the present study for approximating coastal aquifer responses to groundwater extraction. Properly trained and tested meta-models are integrated with a Controlled Elitist Multiple Objective Genetic Algorithm (CEMOGA) within a coupled S-O approach. In each iteration of the optimization algorithm, the meta-models are used to compute the corresponding salinity concentrations for a set of candidate pumping patterns generated by the optimization algorithm. Upon convergence, the non-dominated global optimal solutions are obtained as the Pareto optimal front, which represents a trade-off between the two conflicting objectives of the pumping management problem. It is observed from the solutions of the meta-model based coupled S-O approach that the considered meta-models are capable of producing a Pareto optimal set of solutions quite accurately. However, each meta-modelling approach has distinct advantages over the others when utilized within the integrated S-O approach. Uncertainties in estimating complex flow and solute transport processes in coastal aquifers demand incorporation of the uncertainties related to some of the model parameters. Multidimensional heterogeneity of aquifer properties such as hydraulic conductivity, compressibility, and bulk density are considered as major sources of uncertainty in groundwater modelling system. Other sources of uncertainty are associated with spatial and temporal variability of hydrologic as well as human interventions, e.g. aquifer recharge and transient groundwater extraction patterns. Different realizations of these uncertain model parameters are obtained from different statistical distributions. FIS based meta-models are advanced to a Genetic Algorithm (GA) tuned hybrid FIS model (GA-FIS), to emulate physical processes of coastal aquifers and to evaluate responses of the coastal aquifers to groundwater extraction under groundwater parameter uncertainty. GA is used to tune the FIS parameters in order to obtain the optimal FIS structure. The GA-FIS models thus obtained are linked externally to the CEMOGA in order to derive an optimal pumping management strategy using the coupled S-O approach. The evaluation results show that the proposed saltwater intrusion management model is able to derive reliable optimal groundwater extraction strategies to control saltwater intrusion for the illustrative multilayered coastal aquifer system. The optimal management strategies obtained as solutions of GA-FIS based management models are shown to be reliable and accurate within the specified ranges of values for different realizations of uncertain groundwater parameters. One of the major concerns of the meta-model based integrated S-O approach is the uncertainty associated with the meta-model predictions. These prediction uncertainties, if not addressed properly, may propagate to the optimization procedures, and may deteriorate the optimality of the solutions. A standalone meta-model, when used within an optimal management model, may result in the optimization routine producing actually suboptimal solutions that may undermine the optimality of the groundwater extraction strategies. Therefore, this study proposes an ensemble approach to address the prediction uncertainties of meta-models. Ensemble is an approach to assimilate multiple similar or different algorithms or base learners (emulators). The basic idea of ensemble lies in developing a more reliable and robust prediction tool that incorporates each individual emulator's unique characteristic in order to predict future scenarios. Each individual member of the ensemble contains different input -output mapping functions. Based on their own mapping functions, these individual emulators provide varied predictions on the response variable. Therefore, the combined prediction of the ensemble is likely to be less biased and more robust, reliable, and accurate than that of any of the individual members of the ensemble. Performance of the ensemble meta-models is evaluated using an illustrative coastal aquifer study area. The results indicate that the meta-model based ensemble modelling approach is able to provide reliable solutions for a multilayered coastal aquifer management problem. Relative sea level rise, providing an additional saline water head at the seaside, has a significant impact on an increase in the salinization process of the coastal aquifers. Although excessive groundwater withdrawal is considered as the major cause of saltwater intrusion, relative sea level rise, in combination with the effect of excessive groundwater pumping, can exacerbate the already vulnerable coastal aquifers. This study incorporates the effects of relative sea level rise on the optimized groundwater extraction values for the specified management period. Variation of water concentrations in the tidal river and seasonal fluctuation of river water stage are also incorporated. Three meta-models are developed from the solution results of the numerical simulation model that simulates the coupled flow and solute transport processes in a coastal aquifer system. The results reveal that the proposed meta-models are capable of predicting density dependent coupled flow and solute transport patterns quite accurately. Based on the comparison results, the best meta-model is selected as a computationally cheap substitute of the simulation model in the coupled S-O based saltwater intrusion management model. The performance of the proposed methodology is evaluated for an illustrative multilayered coastal aquifer system in which the effect of climate change induced sea level rise is incorporated for the specified management period. The results show that the proposed saltwater intrusion management model provides acceptable, accurate, and reliable solutions while significantly improving computational efficiency in the coupled S-O methodology. The success of the developed management strategy largely depends on how accurately the prescribed management policy is implemented in real life situations. The actual implementation of a prescribed management strategy often differs from the prescribed planned strategy due to various uncertainties in predicting the consequences, as well as practical constraints, including noncompliance with the prescribed strategy. This results in actual consequences of a management strategy differing from the intended results. To bring the management consequences closer to the intended results, adaptive management strategies can be sequentially modified at different stages of the management horizon using feedback measurements from a deigned monitoring network. This feedback information can be the actual spatial and temporal concentrations resulting from the implementation of actual management strategy. Therefore, field-scale compliance of the developed coastal aquifer management strategy is a crucial aspect of an optimally designed groundwater extraction policy. A 3-D compliance monitoring network design methodology is proposed in this study in order to develop an adaptive and sequentially modified management policy, which aims to improve optimal and justifiable use of groundwater resources in coastal aquifers. In the first step, an ensemble meta-model based multiple objective prescriptive model is developed using a coupled S-O approach in order to derive a set of Pareto optimal groundwater extraction strategies. Prediction uncertainty of meta-models is addressed by utilizing a weighted average ensemble using Set Pair Analysis. In the second step, a monitoring network is designed for evaluating the compliance of the implemented strategies with the prescribed management goals due to possible uncertainties associated with field-scale application of the proposed management policy. Optimal monitoring locations are obtained by maximizing Shannon's entropy between the saltwater concentrations at the selected potential locations. Performance of the proposed 3-D sequential compliance monitoring network design is assessed for an illustrative multilayered coastal aquifer study area. The performance evaluations show that sequential improvements of optimal management strategy are possible by utilizing saltwater concentrations measurements at the proposed optimal compliance monitoring locations. The integrated S-O approach is used to develop a saltwater intrusion management model for a real world coastal aquifer system in the Barguna district of southern Bangladesh. The aquifer processes are simulated by using a 3-D finite element based combined flow and solute transport numerical code. The modelling and management of seawater intrusion processes are performed based on very limited hydrogeological data. The model is calibrated with respect to hydraulic heads for a period of five years from April 2010 to April 2014. The calibrated model is validated for the next three-year period from April 2015 to April 2017. The calibrated and partially validated model is then used within the integrated S-O approach to develop optimal groundwater abstraction patterns to control saltwater intrusion in the study area. Computational efficiency of the management model is achieved by using a MARS based meta-model approximately emulating the combined flow and solute transport processes of the study area. This limited evaluation demonstrates that a planned transient groundwater abstraction strategy, acquired as solution results of a meta-model based integrated S-O approach, is a useful management strategy for optimized water abstraction and saltwater intrusion control. This study shows the capability of the MARS meta-model based integrated S-O approach to solve real-life complex management problems in an efficient manner

Modeling contaminant transport and fate and subsequent impacts on ecosystems

Assessing risks associated with the release of metals into the environment and managing remedial activities requires simulation tools that depict speciation and risk with accurate mechanistic models and well-defined transport parameters. Such tools need to address the following processes: (1) aqueous speciation, (2) distribution mechanisms, (3) transport, and (4) ecological risk. The primary objective of this research is to develop a simulation tool that accounts for these processes. Speciation in the aqueous phase can be assessed with geochemical equilibrium models, such as MINEQL+. Furthermore, metal distribution can be addressed mechanistically. Studies with Pb sorption to amorphous aluminum (HAG), iron (HFO), and manganese (HMO) oxides, as well as oxide coatings, demonstrated that intraparticle diffusion is the rate-limiting mechanism in the sorption process, where best-fit surface diffusivities ranged from 10-18 to 10-15 cm2 s-1 Intraparticle surface diffusion was incorporated into the Groundwater Modeling System (GMS) to accurately simulate metal contaminant mobility where oxides are present. In the model development, the parabolic concentration layer approximation and the operator split technique were used to solve the microscopic diffusion equation coupled with macroscopic advection and dispersion. The resulting model was employed for simulating Sr90 mobility at the U.S. Department of Energy (DOE) Hanford Site. The Sr90 plume is observed to be migrating out of the 100-N area extending into other areas of the Hanford Site and beyond. Once bioavailability is understood, static or dynamic ecological risk assessments can be conducted. Employing the ERA model, a static ecological risk assessment for exposure to depleted uranium (DU) at Aberdeen and Yuma Proving Grounds (APG and YPG) revealed that a reduction in plant root weight is considered likely to occur. For most terrestrial animals at YPG, the predicted DU dose is less than that which would result in a decrease in offspring. However, for the lesser long-nosed bat, reproductive effects are expected to occur through the reduction in size and weight of offspring. At APG, based on very limited data, it is predicted that uranium uptake will not likely affect survival of terrestrial animals and aquatic species. In model validation, sampling of pocket mice, kangaroo rat, white-throated woodrat, deer, and milfoil showed that body burden concentrations fall into the distributions simulated at both sites. This static risk assessment provides a solid background for applying the dynamic approach. Overall, this research contributes to a holistic approach in developing accurate mechanistic models for simulating metal contaminant mobility and bioavailability in subsurface environments

Protein-protein interactions: impact of solvent and effects of fluorination

Proteins have an indispensable role in the cell. They carry out a wide variety of structural, catalytic and signaling functions in all known biological systems. To perform their biological functions, proteins establish interactions with other bioorganic molecules including other proteins. Therefore, protein-protein interactions is one of the central topics in molecular biology. My thesis is devoted to three different topics in the field of protein-protein interactions. The first one focuses on solvent contribution to protein interfaces as it is an important component of protein complexes. The second topic discloses the structural and functional potential of fluorine's unique properties, which are attractive for protein design and engineering not feasible within the scope of canonical amino acids. The last part of this thesis is a study of the impact of charged amino acid residues within the hydrophobic interface of a coiled-coil system, which is one of the well-established model systems for protein-protein interactions studies. I. The majority of proteins interact in vivo in solution, thus studies of solvent impact on protein-protein interactions could be crucial for understanding many processes in the cell. However, though solvent is known to be very important for protein-protein interactions in terms of structure, dynamics and energetics, its effects are often disregarded in computational studies because a detailed solvent description requires complex and computationally demanding approaches. As a consequence, many protein residues, which establish water-mediated interactions, are neither considered in an interface definition. In the previous work carried out in our group the protein interfaces database (SCOWLP) has been developed. This database takes into account interfacial solvent and based on this classifies all interfacial protein residues of the PDB into three classes based on their interacting properties: dry (direct interaction), dual (direct and water-mediated interactions), and wet spots (residues interacting only through one water molecule). To define an interaction SCOWLP considers a donor–acceptor distance for hydrogen bonds of 3.2 Å, for salt bridges of 4 Å, and for van der Waals contacts the sum of the van der Waals radii of the interacting atoms. In previous studies of the group, statistical analysis of a non-redundant protein structure dataset showed that 40.1% of the interfacial residues participate in water-mediated interactions, and that 14.5% of the total residues in interfaces are wet spots. Moreover, wet spots have been shown to display similar characteristics to residues contacting water molecules in cores or cavities of proteins. The goals of this part of the thesis were: 1. to characterize the impact of solvent in protein-protein interactions 2. to elucidate possible effects of solvent inclusion into the correlated mutations approach for protein contacts prediction To study solvent impact on protein interfaces a molecular dynamics (MD) approach has been used. This part of the work is elaborated in section 2.1 of this thesis. We have characterized properties of water-mediated protein interactions at residue and solvent level. For this purpose, an MD analysis of 17 representative complexes from SH3 and immunoglobulin protein families has been performed. We have shown that the interfacial residues interacting through a single water molecule (wet spots) are energetically and dynamically very similar to other interfacial residues. At the same time, water molecules mediating protein interactions have been found to be significantly less mobile than surface solvent in terms of residence time. Calculated free energies indicate that these water molecules should significantly affect formation and stability of a protein-protein complex. The results obtained in this part of the work also suggest that water molecules in protein interfaces contribute to the conservation of protein interactions by allowing more sequence variability in the interacting partners, which has important implications for the use of the correlated mutations concept in protein interactions studies. This concept is based on the assumption that interacting protein residues co-evolve, so that a mutation in one of the interacting counterparts is compensated by a mutation in the other. The study presented in section 2.2 has been carried out to prove that an explicit introduction of solvent into the correlated mutations concept indeed yields qualitative improvement of existing approaches. For this, we have used the data on interfacial solvent obtained from the SCOWLP database (the whole PDB) to construct a “wet” similarity matrix. This matrix has been used for prediction of protein contacts together with a well-established “dry” matrix. We have analyzed two datasets containing 50 domains and 10 domain pairs, and have compared the results obtained by using several combinations of both “dry” and “wet” matrices. We have found that for predictions for both intra- and interdomain contacts the introduction of a combination of a “dry” and a “wet” similarity matrix improves the predictions in comparison to the “dry” one alone. Our analysis opens up the idea that the consideration of water may have an impact on the improvement of the contact predictions obtained by correlated mutations approaches. There are two principally novel aspects in this study in the context of the used correlated mutations methodology : i) the first introduction of solvent explicitly into the correlated mutations approach; ii) the use of the definition of protein-protein interfaces, which is essentially different from many other works in the field because of taking into account physico-chemical properties of amino acids and not being exclusively based on distance cut-offs. II. The second part of the thesis is focused on properties of fluorinated amino acids in protein environments. In general, non-canonical amino acids with newly designed side-chain functionalities are powerful tools that can be used to improve structural, catalytic, kinetic and thermodynamic properties of peptides and proteins, which otherwise are not feasible within the use of canonical amino acids. In this context fluorinated amino acids have increasingly gained in importance in protein chemistry because of fluorine's unique properties: high electronegativity and a small atomic size. Despite the wide use of fluorine in drug design, properties of fluorine in protein environments have not been yet extensively studied. The aims of this part of the dissertation were: 1. to analyze the basic properties of fluorinated amino acids such as electrostatic and geometric characteristics, hydrogen bonding abilities, hydration properties and conformational preferences (section 3.1) 2. to describe the behavior of fluorinated amino acids in systems emulating protein environments (section 3.2, section 3.3) First, to characterize fluorinated amino acids side chains we have used fluorinated ethane derivatives as their simplified models and applied a quantum mechanics approach. Properties such as charge distribution, dipole moments, volumes and size of the fluoromethylated groups within the model have been characterized. Hydrogen bonding properties of these groups have been compared with the groups typically presented in natural protein environments. We have shown that hydrogen and fluorine atoms within these fluoromethylated groups are weak hydrogen bond donors and acceptors. Nevertheless they should not be disregarded for applications in protein engineering. Then, we have implemented four fluorinated L-amino acids for the AMBER force field and characterized their conformational and hydration properties at the MD level. We have found that hydrophobicity of fluorinated side chains grows with the number of fluorine atoms and could be explained in terms of high electronegativity of fluorine atoms and spacial demand of fluorinated side-chains. These data on hydration agrees with the results obtained in the experimental work performed by our collaborators. We have rationally engineered systems that allow us to study fluorine properties and extract results that could be extrapolated to proteins. For this, we have emulated protein environments by introducing fluorinated amino acids into a parallel coiled-coil and enzyme-ligand chymotrypsin systems. The results on fluorination effect on coiled-coil dimerization and substrate affinities in the chymotrypsin active site obtained by MD, molecular docking and free energy calculations are in strong agreement with experimental data obtained by our collaborators. In particular, we have shown that fluorine content and position of fluorination can considerably change the polarity and steric properties of an amino acid side chain and, thus, can influence the properties that a fluorinated amino acid reveals within a native protein environment. III. Coiled-coils typically consist of two to five right-handed α-helices that wrap around each other to form a left-handed superhelix. The interface of two α-helices is usually represented by hydrophobic residues. However, the analysis of protein databases revealed that in natural occurring proteins up to 20% of these positions are populated by polar and charged residues. The impact of these residues on stability of coiled-coil system is not clear. MD simulations together with free energy calculations have been utilized to estimate favourable interaction partners for uncommon amino acids within the hydrophobic core of coiled-coils (Chapter 4). Based on these data, the best hits among binding partners for one strand of a coiled-coil bearing a charged amino acid in a central hydrophobic core position have been selected. Computational data have been in agreement with the results obtained by our collaborators, who applied phage display technology and CD spectroscopy. This combination of theoretical and experimental approaches allowed to get a deeper insight into the stability of the coiled-coil system. To conclude, this thesis widens existing concepts of protein structural biology in three areas of its current importance. We expand on the role of solvent in protein interfaces, which contributes to the knowledge of physico-chemical properties underlying protein-protein interactions. We develop a deeper insight into the understanding of the fluorine's impact upon its introduction into protein environments, which may assist in exploiting the full potential of fluorine's unique properties for applications in the field of protein engineering and drug design. Finally we investigate the mechanisms underlying coiled-coil system folding. The results presented in the thesis are of definite importance for possible applications (e.g. introduction of solvent explicitly into the scoring function) into protein folding, docking and rational design methods. The dissertation consists of four chapters: ● Chapter 1 contains an introduction to the topic of protein-protein interactions including basic concepts and an overview of the present state of research in the field. ● Chapter 2 focuses on the studies of the role of solvent in protein interfaces. ● Chapter 3 is devoted to the work on fluorinated amino acids in protein environments. ● Chapter 4 describes the study of coiled-coils folding properties. The experimental parts presented in Chapters 3 and 4 of this thesis have been performed by our collaborators at FU Berlin. Sections 2.1, 2.2, 3.1, 3.2 and Chapter 4 have been submitted/published in peer-reviewed international journals. Their organization follows a standard research article structure: Abstract, Introduction, Methodology, Results and discussion, and Conclusions. Section 3.3, though not published yet, is also organized in the same way. The literature references are summed up together at the end of the thesis to avoid redundancy within different chapters

Optimisation of welding parameters to mitigate the effect of residual stress on the fatigue life of nozzle–shell welded joints in cylindrical pressure vessels.

Doctoral Degree. University of KwaZulu-Natal, Durban.The process of welding steel structures inadvertently causes residual stress as a result of thermal cycles that the material is subjected to. These welding-induced residual stresses have been shown to be responsible for a number of catastrophic failures in critical infrastructure installations such as pressure vessels, ship’s hulls, steel roof structures, and others. The present study examines the relationship between welding input parameters and the resultant residual stress, fatigue properties, weld bead geometry and mechanical properties of welded carbon steel pressure vessels. The study focuses on circumferential nozzle-to-shell welds, which have not been studied to this extent until now. A hybrid methodology including experimentation, numerical analysis, and mathematical modelling is employed to map out the relationship between welding input parameters and the output weld characteristics in order to further optimize the input parameters to produce an optimal welded joint whose stress and fatigue characteristics enhance service life of the welded structure. The results of a series of experiments performed show that the mechanical properties such as hardness are significantly affected by the welding process parameters and thereby affect the service life of a welded pressure vessel. The weld geometry is also affected by the input parameters of the welding process such that bead width and bead depth will vary depending on the parametric combination of input variables. The fatigue properties of a welded pressure vessel structure are affected by the residual stress conditions of the structure. The fractional factorial design technique shows that the welding current (I) and voltage (V) are statistically significant controlling parameters in the welding process. The results of the neutron diffraction (ND) tests reveal that there is a high concentration of residual stresses close to the weld centre-line. These stresses subside with increasing distance from the centre-line. The resultant hoop residual stress distribution shows that the hoop stresses are highly tensile close to the weld centre-line, decrease in magnitude as the distance from the weld centre-line increases, then decrease back to zero before changing direction to compressive further away from the weld centre-line. The hoop stress distribution profile on the flange side is similar to that of the pipe side around the circumferential weld, and the residual stress peak values are equal to or higher than the yield strength of the filler material. The weld specimens failed at the weld toe where the hoop stress was generally highly tensile in most of the welded specimens. The multiobjective genetic algorithm is successfully used to produce a set of optimal solutions that are in agreement with values obtained during experiments. The 3D finite element model produced using MSC Marc software is generally comparable to physical experimentation. The results obtained in the present study are in agreement with similar studies reported in the literature