Supplier codes of conduct: Acting responsibly in global supply chains

Nowadays the Western companies are considered responsible for the social and environmental issues in their whole supply chains. To influence the practices of their suppliers the Western companies have created suppliers codes of conduct (SCCs) which express their requirements. Suppliers’ compliance with the SCCs is checked through audits. The purpose of this thesis is to analyze SCCs as a means for Western companies to ensure socially and environmentally responsible actions in their global supply chains, and the sub-objectives are to find out 1) how well do the SCCs and their auditing work at suppliers’ production sites and 2) how can possible problems related to SCCs and their auditing be solved. This is a qualitative research carried out in the form of a case study with two case companies. In this study both primary and secondary data is used. The primary data is collected in the form of interviews of the case company representatives and three external experts. Based on a theoretical framework of previous research in the fields of corporate social responsibility and supply chain management, a model with eleven factors, which influence the success of SCC implementation and the auditing of SCC –implementation, is drafted. Also several different best-practices to help to solve and avoid possible problems related to SCC -implementation and auditing have been identified from previous research. Based on the findings of this study the theoretical model has been updated adding two new influential factors. It seems that how well the SCC and its auditing work at suppliers’ production sites depends on the joint effect of thirteen influential factors: buyer’s purchasing policy, supplier’s motivation, buyer’s commitment, the solving of agency problems, the contents of the SCC, supplier’s role and the buyer-supplier –relationship, complexity of supply chain, the limitations of the smaller buyers, cooperation through a business association or multi-stakeholder system, the role of supplier’s employees, SCC –related communication and supplier’s understanding, cheating in audits and the auditors. The possible problems related to SCCs and their auditing can be solved by adopting best-practices. Nine of the theoretical best-practices stand out from the findings of this study: 1) two-way communication and collecting feedback from suppliers, 2) the philosophy of continuous improvement, 3) long-term business relationships with the supplier, 4) informing the supplier about the advantages of SCC –compliance, 5) rewarding code-compliant suppliers, 6) building collaborative, good buyer-supplier relationships, 7) supporting and advising the supplier, 8) joining a business association or multi-stakeholder system and 9) interviewing supplier’s employees as a part of the audits.

Adaptive control of Hydraulic Drive

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for the Hydraulic Drive. The calculation needed and the modeling were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™ etc. In the work there was applied the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial. The intelligent adaptive to nonlinearities algorithm for solving Lyapunov’s equation was developed. Developed algorithm works properly but considered plant is not met requirement of functioning with. The results showed confirmation that adaptive systems application significantly increases possibilities in use devices and might be used for correction a system’s behavior dynamics.

Intelligent mapping of stresses in a hydraulic crane

Continuous loading and unloading can cause breakdown of cranes. In seeking solution to this problem, the use of an intelligent control system for improving the fatigue life of cranes in the control of mechatronics has been under study since 1994. This research focuses on the use of neural networks as possibilities of developing algorithm to map stresses on a crane. The intelligent algorithm was designed to be a part of the system of a crane, the design process started with solid works, ANSYS and co-simulation using MSc Adams software which was incorporated in MATLAB-Simulink and finally MATLAB neural network (NN) for the optimization process. The flexibility of the boom accounted for the accuracy of the maximum stress results in the ADAMS model. The flexibility created in ANSYS produced more accurate results compared to the flexibility model in ADAMS/View using discrete link. The compatibility between.ADAMS and ANSYS softwares was paramount in the efficiency and the accuracy of the results. Von Mises stresses analysis was more suitable for this thesis work because the hydraulic boom was made from construction steel FE-510 of steel grade S355 with yield strength of 355MPa. Von Mises theory was good for further analysis due to ductility of the material and the repeated tensile and shear loading. Neural network predictions for the maximum stresses were then compared with the co-simulation results for accuracy, and the comparison showed that the results obtained from neural network model were sufficiently accurate in predicting the maximum stresses on the boom than co-simulation.

Thermal reactions of the major hydrocarbon components of biomass gasification gas

Gasification of biomass is an efficient method process to produce liquid fuels, heat and electricity. It is interesting especially for the Nordic countries, where raw material for the processes is readily available. The thermal reactions of light hydrocarbons are a major challenge for industrial applications. At elevated temperatures, light hydrocarbons react spontaneously to form higher molecular weight compounds. In this thesis, this phenomenon was studied by literature survey, experimental work and modeling effort. The literature survey revealed that the change in tar composition is likely caused by the kinetic entropy. The role of the surface material is deemed to be an important factor in the reactivity of the system. The experimental results were in accordance with previous publications on the subject. The novelty of the experimental work lies in the used time interval for measurements combined with an industrially relevant temperature interval. The aspects which are covered in the modeling include screening of possible numerical approaches, testing of optimization methods and kinetic modelling. No significant numerical issues were observed, so the used calculation routines are adequate for the task. Evolutionary algorithms gave a better performance combined with better fit than the conventional iterative methods such as Simplex and Levenberg-Marquardt methods. Three models were fitted on experimental data. The LLNL model was used as a reference model to which two other models were compared. A compact model which included all the observed species was developed. The parameter estimation performed on that model gave slightly impaired fit to experimental data than LLNL model, but the difference was barely significant. The third tested model concentrated on the decomposition of hydrocarbons and included a theoretical description of the formation of carbon layer on the reactor walls. The fit to experimental data was extremely good. Based on the simulation results and literature findings, it is likely that the surface coverage of carbonaceous deposits is a major factor in thermal reactions.

On Distributed Storage Codes

Distributed storage systems are studied. The interest in such system has become relatively wide due to the increasing amount of information needed to be stored in data centers or different kinds of cloud systems. There are many kinds of solutions for storing the information into distributed devices regarding the needs of the system designer. This thesis studies the questions of designing such storage systems and also fundamental limits of such systems. Namely, the subjects of interest of this thesis include heterogeneous distributed storage systems, distributed storage systems with the exact repair property, and locally repairable codes. For distributed storage systems with either functional or exact repair, capacity results are proved. In the case of locally repairable codes, the minimum distance is studied. Constructions for exact-repairing codes between minimum bandwidth regeneration (MBR) and minimum storage regeneration (MSR) points are given. These codes exceed the time-sharing line of the extremal points in many cases. Other properties of exact-regenerating codes are also studied. For the heterogeneous setup, the main result is that the capacity of such systems is always smaller than or equal to the capacity of a homogeneous system with symmetric repair with average node size and average repair bandwidth. A randomized construction for a locally repairable code with good minimum distance is given. It is shown that a random linear code of certain natural type has a good minimum distance with high probability. Other properties of locally repairable codes are also studied.

Electromagnetic and thermal design of a multilevel converter with high power density and reliability

Electric energy demand has been growing constantly as the global population increases. To avoid electric energy shortage, renewable energy sources and energy conservation are emphasized all over the world. The role of power electronics in energy saving and development of renewable energy systems is significant. Power electronics is applied in wind, solar, fuel cell, and micro turbine energy systems for the energy conversion and control. The use of power electronics introduces an energy saving potential in such applications as motors, lighting, home appliances, and consumer electronics. Despite the advantages of power converters, their penetration into the market requires that they have a set of characteristics such as high reliability and power density, cost effectiveness, and low weight, which are dictated by the emerging applications. In association with the increasing requirements, the design of the power converter is becoming more complicated, and thus, a multidisciplinary approach to the modelling of the converter is required. In this doctoral dissertation, methods and models are developed for the design of a multilevel power converter and the analysis of the related electromagnetic, thermal, and reliability issues. The focus is on the design of the main circuit. The electromagnetic model of the laminated busbar system and the IGBT modules is established with the aim of minimizing the stray inductance of the commutation loops that degrade the converter power capability. The circular busbar system is proposed to achieve equal current sharing among parallel-connected devices and implemented in the non-destructive test set-up. In addition to the electromagnetic model, a thermal model of the laminated busbar system is developed based on a lumped parameter thermal model. The temperature and temperature-dependent power losses of the busbars are estimated by the proposed algorithm. The Joule losses produced by non-sinusoidal currents flowing through the busbars in the converter are estimated taking into account the skin and proximity effects, which have a strong influence on the AC resistance of the busbars. The lifetime estimation algorithm was implemented to investigate the influence of the cooling solution on the reliability of the IGBT modules. As efficient cooling solutions have a low thermal inertia, they cause excessive temperature cycling of the IGBTs. Thus, a reliability analysis is required when selecting the cooling solutions for a particular application. The control of the cooling solution based on the use of a heat flux sensor is proposed to reduce the amplitude of the temperature cycles. The developed methods and models are verified experimentally by a laboratory prototype.

Permanent magnet synchronous generator design solution for large directdrive wind turbines: Thermal behavior of the LC DD-PMSG

Wind is one of the most compelling forms of indirect solar energy. Available now, the conversion of wind power into electricity is and will continue to be an important element of energy self-sufficiency planning. This paper is one in a series intended to report on the development of a new type of generator for wind energy; a compact, high-power, direct-drive permanent magnet synchronous generator (DD-PMSG) that uses direct liquid cooling (LC) of the stator windings to manage Joule heating losses. The main param-eters of the subject LC DD-PMSG are 8 MW, 3.3 kV, and 11 Hz. The stator winding is cooled directly by deionized water, which flows through the continuous hollow conductor of each stator tooth-coil winding. The design of the machine is to a large degree subordinate to the use of these solid-copper tooth-coils. Both steady-state and timedependent temperature distributions for LC DD-PMSG were examined with calculations based on a lumpedparameter thermal model, which makes it possible to account for uneven heat loss distribution in the stator conductors and the conductor cooling system. Transient calculations reveal the copper winding temperature distribution for an example duty cycle during variable-speed wind turbine operation. The cooling performance of the liquid cooled tooth-coil design was predicted via finite element analysis. An instrumented cooling loop featuring a pair of LC tooth-coils embedded in a lamination stack was built and laboratory tested to verify the analytical model. Predicted and measured results were in agreement, confirming the predicted satisfactory operation of the LC DD-PMSG cooling technology approach as a whole.

Energy evaluation of an evaporative cooling system using water driven ejector

The search for efficient and accessible cooling systems has increased worldwide. This study aims to build and evaluate an evaporative cooling system using a water driven ejector, allowing it to be installed in places with plenty of water. The system was investigated varying the flow rate and temperature of the circulating water, temperature of the replacement water, and coefficient of performance. The best vacuum obtained was 8.5 kPa at nominal operating conditions of 4.1 ± 0.1 m³/h and 5 ± 0.5 ºC for the circulating water reaching the temperature of 9.7 ± 0.5 ºC. The pulse-like disturbance generated by replacing the cooling water at different periods of times did not result in significant affect vacuum destabilization and the temperature rise in the cooling tank. The coefficient of performance of the system at the highest thermal power of 92.27 W was 0.077, which was underestimated due to possible problems related to pump efficiency. The system evaluated under the conditions proposed can be very efficient for cooling fluids at higher temperatures, and it can be complementary to main refrigeration systems.

Thermal modelling of commercial lithium-ion batteries

The accelerating adoption of electrical technologies in vehicles over the recent years has led to an increase in the research on electrochemical energy storage systems, which are among the key elements in these technologies. The application of electrochemical energy storage systems for instance in hybrid electrical vehicles (HEVs) or hybrid mobile working machines allows tolerating high power peaks, leading to an opportunity to downsize the internal combustion engine and reduce fuel consumption, and therefore, CO2 and other emissions. Further, the application of electrochemical energy storage systems provides an option of kinetic and potential energy recuperation. Presently, the lithium-ion (Li-ion) battery is considered the most suitable electrochemical energy storage type in HEVs and hybrid mobile working machines. However, the intensive operating cycle produces high heat losses in the Li-ion battery, which increase its operating temperature. The Li-ion battery operation at high temperatures accelerates the ageing of the battery, and in the worst case, may lead to a thermal runaway and fire. Therefore, an appropriate Li-ion battery cooling system should be provided for the temperature control in applications such as HEVs and mobile working machines. In this doctoral dissertation, methods are presented to set up a thermal model of a single Li-ion cell and a more complex battery module, which can be used if full information about the battery chemistry is not available. In addition, a non-destructive method is developed for the cell thermal characterization, which allows to measure the thermal parameters at different states of charge and in different points of cell surface. The proposed models and the cell thermal characterization method have been verified by experimental measurements. The minimization of high thermal non-uniformity, which was detected in the pouch cell during its operation with a high C-rate current, was analysed by applying a simplified pouch cell 3D thermal model. In the analysis, heat pipes were incorporated into the pouch cell cooling system, and an optimization algorithm was generated for the estimation of the optimalplacement of heat pipes in the pouch cell cooling system. An analysis of the application of heat pipes to the pouch cell cooling system shows that heat pipes significantly decrease the temperature non-uniformity on the cell surface, and therefore, heat pipes were recommended for the enhancement of the pouch cell cooling system.

Hydraulic and fluvial geomorphological models for a bedrock channel reach of the Twenty Mile Creek /

Bedrock channels have been considered challenging geomorphic settings for the application of numerical models. Bedrock fluvial systems exhibit boundaries that are typically less mobile than alluvial systems, yet they are still dynamic systems with a high degree of spatial and temporal variability. To understand the variability of fluvial systems, numerical models have been developed to quantify flow magnitudes and patterns as the driving force for geomorphic change. Two types of numerical model were assessed for their efficacy in examining the bedrock channel system consisting of a high gradient portion of the Twenty Mile Creek in the Niagara Region of Ontario, Canada. A one-dimensional (1-D) flow model that utilizes energy equations, HEC RAS, was used to determine velocity distributions through the study reach for the mean annual flood (MAF), the 100-year return flood and the 1,000-year return flood. A two-dimensional (2-D) flow model that makes use of Navier-Stokes equations, RMA2, was created with the same objectives. The 2-D modeling effort was not successful due to the spatial complexity of the system (high slope and high variance). The successful 1 -D model runs were further extended using very high resolution geospatial interpolations inherent to the HEC RAS extension, HEC geoRAS. The modeled velocity data then formed the basis for the creation of a geomorphological analysis that focused upon large particles (boulders) and the forces needed to mobilize them. Several existing boulders were examined by collecting detailed measurements to derive three-dimensional physical models for the application of fluid and solid mechanics to predict movement in the study reach. An imaginary unit cuboid (1 metre by 1 metre by 1 metre) boulder was also envisioned to determine the general propensity for the movement of such a boulder through the bedrock system. The efforts and findings of this study provide a standardized means for the assessment of large particle movement in a bedrock fluvial system. Further efforts may expand upon this standardization by modeling differing boulder configurations (platy boulders, etc.) at a high level of resolution.

A study of the thermal decomposition of allyl t-butyl peroxide and 3-hydroperoxy-1-propene (allyl hydroperoxide) in toluene /|nby Krishnankutty Nair V. G. -- 260 St. Catharines [Ont. : s. n.],

Kinetics and product studies of the decompositions of allyl-t-butyl peroxide and 3-hydroperoxy- l-propene (allyl hydroperoxide ) in tolune were investigated. Decompositions of allyl-t-butyl peroxide in toluene at 130-1600 followed first order kinetics with an activation energy of 32.8 K.cals/mol and a log A factor of 13.65. The rates of decomposition were lowered in presence of the radical trap~methyl styrene. By the radical trap method, the induced decomposition at 1300 is shown to be 12.5%. From the yield of 4-phenyl-l,2- epoxy butane the major path of induced decomposition is shown to be via an addition mechanism. On the other hand, di-t-butYl peroxyoxalate induced decomposition of this peroxide at 600 proceeded by an abstraction mechanism. Induced decomposition of peroxides and hydroperoxides containing the allyl system is proposed to occur mainly through an addition mechanism at these higher temperatures. Allyl hydroperoxide in toluene at 165-1850 decomposes following 3/2 order kinetics with an Ea of 30.2 K.cals per mole and log A of 10.6. Enormous production of radicals through chain branching may explain these relatively low values of E and log A. The complexity of the reaction is indicated a by the formation of various products of the decomposition. A study of the radical attack of the hydro peroxide at lower temperatures is suggested as a further work to throw more light on the nature of decomposition of this hydroperoxide.

Solvent Effect on Absolute Fluorescence Quantum Yield of Rhodamine 6G Determined Using Transient Thermal Lens Technique

Dual beam thermal lens tecbnique is successfully employed for the determination of absolute Fluorescence quantum yield of rhodamine 6G lnser dye in different solvents. A 532 nm radiation from a Q-switched Nd:YAG laser was used for the excitation purpose. The fluorescence quantum yield values are found to be strongly influenced by environmental effects. It has been observed that fluorescence yield is greater for rhodamine 6G in ethylene glycol system than in water or in methanol. Our results also indicate that parameters like concentration of the dye solution, aggregate formation and excited state absorption affect the absolute values of fluorescence yield significantly.

Photopyroelectric study of thermal properties and phase transitions in selected solids

This thesis presents in detail. the theoretical developments and calculations which are used for the simultaneous determination of thermal parameters, namely thermal diffusivity (a). thermal effusivity (e), thermal conductivity (K) and heat capacity (cr ) employing photopyroelectric technique. In our calculations. we have assumed that the pyroelectric detector is supported on a copper backing. so that there will be sufficient heat exchange between the heated pyroelectric detector and the backing so that the signal fluctuations are reduced to a minimum. Since the PPE signal depends on the properties of the detector that are also temperature dependent. a careful temperature calibration of the system need to be carried out. APPE cell has been fabricated for the measurements that can be used to measure the thermal properties of solid samples from ~ 90 K to ~ 350 K. The cell has been calibrated using standard samples and the accuracy of the technique is found to be of the order of± 1%.In this thesis, we have taken up work n photopyroelectric investigation of thermal parameters of ferroelectric crystals such as Glycine phosphite (NH3CH2COOH3P03), Triglycine sulfate and Thiourea as well as mixed valence perovskites samples such as Lead doped Lanthanum Manganate (Lal_xPb~Mn03) Calcium doped (Lal_xCaxMnOJ) and Nickel doped Lanthanum Stroncium Cobaltate (Lao~Sro5Ni,Col_x03).The three ferroelectric crystals are prepared by the slow evaporation technique and the mixed valence perovskites by solid state reaction technique.Mixed valence perovskites, with the general formula RI_xA~Mn03 (R = La. Nd or Pr and A = Ba, Ca, Sr or Pb) have been materials of intense experimental and theoretical studies over the past few years. These materials show . colossal magneloresis/ance' (CMR) in samples with 0.2 < x < 0.5 in such a doping region, resistivity exhibits a peak at T = T p' the metal - insulator transition temperature. The system exhibits metallic characteristics with d %T > Oabove Tp (wherep is the resistivity) and insulating characteristics with d % T < 0 above T p. Despite intensive investigations on the CMR phenomena and associated electrical properties. not much work has been done on the variation of thermal properties of these samples. We have been quite successful in finding out the nature of anomaly associated with thermal properties when the sample undergoes M-I transition.The ferroelectric crystal showing para-ferroelectric phase transitions - Glycine phosphite. Thiourea and Triglycine sulfate - are studied in detail in order to see how well the PPE technique enables one to measure the thermal parameters during phase transitions. It is seen that the phase transition gets clearly reflected in the variation of thermal parameters. The anisotropy in thermal transport along different crystallographic directions are explained in terms of the elastic anisotropy and lattice contribution to the thermal conductivity. Interesting new results have been obtained on the above samples and are presented in three different chapters of the thesis.In summary. we have carried investigations of the variations of the thermal parameters during phase transitions employing photopyroelectric technique. The results obtained on different systems are important not only in understanding the physics behind the transitions but also in establishing the potentiality of the PPE tool. The full potential of PPE technique for the investigation of optical and thermal properties of materials still remains to be taken advantage of by workers in this field.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Kinetics of martensitic transitions in Cu-Al-Mn under thermal cycling: Analysis at multiple length scales

In this paper we study the evolution of the kinetic features of the martensitic transition in a Cu-Al-Mn single crystal under thermal cycling. The use of several experimental techniques including optical microscopy, calorimetry, and acoustic emission, has enabled us to perform an analysis at multiple scales. In particular, we have focused on the analysis of avalanche events (associated with the nucleation and growth of martensitic domains), which occur during the transition. There are significant differences between the kinetics at large and small length scales. On the one hand, at small length scales, small avalanche events tend to sum to give new larger events in subsequent loops. On the other hand, at large length scales the large domains tend to split into smaller ones on thermal cycling. We suggest that such different behavior is the necessary ingredient that leads the system to the final critical state corresponding to a power-law distribution of avalanches.