Thesis (MScIng)--University of Stellenbosch, 2003. / ENGLISH ABSTRACT: The analysis of process data obtained from chemical and metallurgical engineering systems is a crucial aspect of the operating of any process, as information extracted from the data is used for control purposes, decision making and forecasting. Singular spectrum analysis (SSA) is a relatively new technique that can be used to decompose time series into their constituent components, after which a variety of further analyses can be applied to the data. The objectives of this study were to investigate the abilities of SSA regarding the filtering of data and the subsequent modelling of the filtered data, to explore the methods available to perform nonlinear SSA and finally to explore the possibilities of Monte Carlo SSA to characterize and identify process systems from observed time series data. Although the literature indicated the widespread application of SSA in other research fields, no previous application of singular spectrum analysis to time series obtained from chemical engineering processes could be found. SSA appeared to have a multitude of applications that could be of great benefit in the analysis of data from process systems. The first indication of this was in the filtering or noise-removal abilities of SSA. A number of case studies were filtered by various techniques related to SSA, after which a number of neural network modelling strategies were applied to the data. It was consistently found that the models built on data that have been prefiltered with SSA outperformed the other models. The effectiveness of localized SSA and auto-associative neural networks in performing nonlinear SSA were compared. Both techniques succeeded in extracting a number of nonlinear components from the data that could not be identified from linear SSA. However, it was found that localized SSA was a more reliable approach, as the auto-associative neural networks would not train for some of the data or extracted nonsensical components for other series. Lastly a number of time series were analysed using Monte Carlo SSA. It was found that, as is the case with all other characterization techniques, Monte Carlo SSA could not succeed in correctly classifying all the series investigated. For this reason several tests were used for the classification of the real process data. In the light of these findings, it was concluded that singular spectrum analysis could be a valuable tool in the analysis of chemical and metallurgical process data. / AFRIKAANSE OPSOMMING: Die analise van chemise en metallurgiese prosesdata wat verkry is vanaf chemiese of metallurgiese ingenieursstelsels is ‘n baie belangrike aspek in die bedryf van enige proses, aangesien die inligting wat van die data onttrek word vir prosesbeheer, besluitneming of die bou van prosesmodelle gebruik kan word. Singuliere spektrale analise is ‘n relatief nuwe tegniek wat gebruik kan word om tydreekse in hul onderliggende komponente te ontbind. Die doelwitte van hierdie studie was om ‘n omvattende literatuuroorsig oor die ontwikkeling van die tegniek en die toepassing daarvan te doen, beide in die ingenieursindustrie en in ander navorsingsvelde, die navors van die moontlikhede van SSA aangaande die verwydering van geraas uit die data en die gevolglike modellering van die skoon data te ondersoek, ‘n ondersoek te doen na sommige van die beskikbare tegnieke vir nie-lineêre SSA en laastens ‘n studie te maak van die potensiaal van Monte Carlo SSA vir die karakterisering en identifikasie van data verkry vanaf prosesstelsels. Ten spyte van aanduidings in die literatuur dat SSA wydverspreid toegepas word in ander navorsingsvelde, kon geen vorige toepassings gevind word van SSA op chemiese prosesse nie. Dit wil voorkom asof die chemiese nywerhede groot baat kan vind by SSA van prosesdata. Die eerste aanduiding van hierdie voordele was in die vermoë van SSA om geraas te verwyder uit tydreekse. ‘n Aantal tipiese gevalle is ondersoek deur van verskeie benaderings tot SSA gebruik te maak. Nadat die geraas uit die tydreekse van die toetsgevalle verwyder is, is neurale netwerke gebruik om die prosesse te modelleer. Daar is herhaaldelik gevind dat die modelle wat gebou is op data wat eers deur SSA skoongemaak is, beter presteer as die wat slegs op die onverwerkte data gepas is. Die effektiwiteit van lokale SSA en auto-assosiatiewe neurale netwerke om nie- lineêre SSA toe te pas is ook vergelyk. Albei tegnieke het daarin geslaag om nie- lineêre hoofkomponente van die data te onttrek wat nie geïdentifiseer kon word deur die lineêre benadering nie. Daar is egter gevind dat lokale SSA ‘n meer betroubare tegniek is, aangesien die autoassosiatiewe neurale netwerke nie op sommige van die datastelle wou leer nie en vir ander tydreekse sinnelose hoofkomponente onttrek het. Laastens is ‘n aantal tydreekse geanaliseer met behulp van Monte Carlo SSA. Soos met alle ander karakteriseringstegnieke, kon Monte Carlo SSA nie daarin slaag om al die tydreekse wat ondersoek is korrek te identifiseer nie. Om hierdie rede is ‘n kombinasie van toetse gebruik om die onbekende tydreekse te klassifiseer. In die lig van al hierdie bevindinge, is die gevolgtrekking gemaak dat singuliere spektrale analise ‘n waardevolle hulpmiddel kan wees in die analise van chemiese en metallurgiese prosesdata.

Thesis (MScIng)--University of Stellenbosch, 2003. / ENGLISH ABSTRACT: Physical systems encountered in process engineering are invariably ill-defined, multivariate, and exhibit complex nonlinear dynamical behaviour. The increasing demands for better process efficiency and high product quality have led to the development and implementation of advanced control strategies in process plants. These modern control strategies are based on the use of a mathematical model defined for the process. Traditionally, linear models have been used to approximate the dynamics of processes whereas most processes are governed by nonlinear mechanisms. Since linear systems theory is well-established whereas nonlinear systems theory is not, recent developments in nonlinear dynamical systems theory present opportunities for improved approaches in modelling these process systems. It is now known that a nonlinear description of a process can be obtained from using time-delayed copies reconstructed from measurements taken from the process. Due to low signal to noise ratios associated with measured data it is logical to exploit redundant information in multivariate time signals taken from the systems in reconstructing the underlying dynamics. This study investigated the extension of univariate nonlinear time series analysis to the situation where multivariate measurements are available. Using simulated data from a coupled continuously stirred tank reactor and measured data from a flotation process system, the comparative advantages of using multivariate and univariate state space reconstructions were investigated. With respect to detection of nonlinearity multivariate surrogate analysis were found to give potentially robust results because of preservation of cross-correlations among components in the surrogate data. Multivariate local linear models showed a deterministic structure in both small and large neighbourhood sizes whereas for scalar embeddings determinism was defined only in smaller neighbourhood sizes. Non-uniform multivariate embeddings gave local linear models that resembled models from a trivial reconstruction of the original state space variables. With regard to global nonlinear modelling, multivariate embeddings gave models with better predictability irrespective of the model class used. Further improvements in the performance of models were obtained for multivariate non-uniform embeddings. A relatively new statistical learning algorithm, the least-squares support vector machine (LSSVM), was evaluated using multilayer perceptrons (MLP) as a benchmark in modelling nonlinear time series using simulated and plant data. It was observed that in the absence of autocorrelations in the variables and sparse data LSSVMs performed better than MLPs. Simulation of trained models gave consistent results for the LSSVMs, which was not the case for MLPs. However, the computational costs incurred in training the LSSVM model was significantly higher than for MLPs. LSSVMs were found to be insensitive to dimensionality reduction methods whereas the performance of MLPs degraded with increasing complexity of the dimension reduction method. No relative merits were found for using complex subspace dimension reduction methods for the data used. No general conclusions could be drawn with respect to the relative superiority of one class of models method over the other. Spatiotemporal structures are routinely observed in many chemical systems, such as reactive-diffusion and other pattern forming systems. We investigated the modelling of spatiotemporal time series using the coupled logistic map lattice as a case study. It was found that including both spatial and temporal information improved the performance of the fitted models. However, the superiority of spatiotemporal embeddings over individual time series was found to be defined for certain choices of the spatial and temporal embedding parameters. / AFRIKAANSE OPSOMMING: Fisiese stelsels wat in prosesingenieurswese voorkom is dikwels nie goed gedefinieer nie, multiveranderlik en vertoon komplekse nie-lineˆere gedrag. Toenemende vereistes vir ho¨e prosesdoeltreffendheid en produkgehalte het gelei tot die ontwikkeling en implementering van gevorderde beheerstrategie¨e vir prosesaanlegte. Hierdie morderne beheerstrategie¨e is gebaseer op die gebruik van wiskundige prosesmodelle. Lineˆere modelle word gewoonlik ontwikkel, al is die onderliggende prosesmeganismes in die algemeen nie-lineˆere, aangesien lineˆere stetselteorie goed gevestig is, en nie-line¨ere stelselteorie nie. Onlangse verwikkelinge in die teorie van nie-lineˆeredinamiese stelsels bied egter geleenthede vir verbeterde modellering van prosesstelsels. Dit is bekend dat ‘n nie-lineˆere beskrywing van ‘n progses verkry kan word deur tydvertraagde kopie¨e van metings van die prosesse te rekonstrueer. Met die lae seintot- geraasverhoudings wat met gemete data geassosieer word, is dit logies om die oortollige informasie in meerveranderlike seine te benut tydens die rekonstruksie van die onderliggende prosesdinamika. In die tesis is die uitbreiding van enkel-veranderlike nie-lineˆere tydreeksontleding na meer-veranderlike stelsels ondersoek. Met data van twee aaneengeskakelde gesimuleerde geroerde tenkreaktore en werklike data van ‘n flottasieproses, is die meriete van enkel- en meerveranderlike rekonstruksies van toestandruimtes ondersoek. Meerveranderlike surrogaatdata-ontleding het nie-lineariteite in die data op ‘n meer robuuste wyse ge¨ıdentifiseer, a.g.v. die behoud van kruis-korrelasies in die komponente van die data. Meerveranderlike lokale lineˆere modelle het ‘n deterministiese struktuur in beide klein en groot naasliggende omgewings ge¨ıdentifiseer, terwyl enkelveranderlike metodes dit slegs vir klein naasliggende omgewings kon doen. Nie-uniforme meerveranderlike inbeddings het lokale lineˆere modelle gegenereer wat soos globale modelle afkomstig van triviale rekonstruksies van die data gelyk het. M.b.t globale nie-lineˆere modellering, het meerveranderlike inbedding deurgaans beter modelle opgelewer. Verdere verbetering in die prestasie van modelle kon verkry word d.m.v. meerveranderlike nie-uniforme inbedding. ‘n Relatief nuwe statistiese algoritme, die kleinste-kwadrate-steunvektormasjien (KKSVM) is ge¨evalueer teenoor multilaag-perseptrons (MLP) as ‘n standaard vir die modellering van nie-lineˆere tydreekse, deur gebruik te maak van gesimuleerde en werklike aanlegdata. Daar is gevind dat die KKSVM beter presteer het as die MLPs wanneer die opeenvolgende waarnemings swak gekorreleer en min was relatief tot die aantal veranderlikes. Die KKSVMs het beduidend langer geneem as die MLPs om te ontwikkel. Hulle was ook minder sensitief vir die metodes wat gevolg is om die dimensionaliteit van die data te verlaag, anders as die MLPs. Ook is gevind dat meer komplekse metodes tot die verlaging van die dimensionaliteit weinig nut gehad het. Geen algemene gevolgtrekkings kan egter gemaak word m.b.t die verskillende modelle nie. Ruimtelik-temporale strukture word algemeen waargeneem in baie chemiese stelsels, soos reaktiewe diffusie e.a. patroonvormende sisteme. Die modellering van ruimtelik-temporale stelsels is bestudeer aan die hand van ‘n gekoppelde logistiese projeksierooster. Insluiting van beide die ruimtelike en temporale inligting het tot beduidend beter modelle gelei, solank as wat di´e inligting op die regte wyse ontsluit is.

Model predictive control has become a standard for most control strategies in modern process plants. It relies heavily on process models, which might not always be fundamentally available, but can be obtained from time series analysis. The first step in any control strategy is to identify or detect changes in the system, if present. The detection of such changes, known as dynamic changes, is the main objective of this study. In the literature a wide range of change detection methods has been developed and documented. Most of these methods assume some prior knowledge of the system, which is not the case in this study. Furthermore a large number of change detection methods based on process history data assume a linear relationship between process variables with some stochastic influence from the environment. These methods are well developed, but fail when applied to nonlinear dynamic systems, which is focused on in this study. A large number of the methods designed for nonlinear systems make use of statistics defined in phase space, which led to the method proposed in this study. The correlation dimension is an invariant measure defined in phase space that is sensitive to dynamic change in the system. The proposed method uses the correlation dimension as test statistic with and moving window approach to detect dynamic changes in nonlinear systems. The proposed method together with two dynamic change detection methods with different approaches was applied to simulated time series data. The first method considered was a change-point algorithm that is based on singular spectrum analysis. The second method applied to the data was mutual cross prediction, which utilises the prediction error from a multilayer perceptron network. After the proposed method was applied to the data the three methods’ performance were evaluated. Time series data were obtained from simulating three systems with mathematical equations and observing one real process, the electrochemical noise produced by a corroding system. The three simulated systems considered in this study are the Belousov-Zhabotinsky reaction, an autocatalytic process and a predatory-prey model. The time series obtained from observing a single variable was considered as the only information available from the systems. Before the change detection methods were applied to the time series data the phase spaces of the systems were reconstructed with time delay embedding. All three the methods were able to do identify the change in dynamics of the time series data. The change-point detection algorithm did however produce a haphazard behaviour of its detection statistic, which led to multiple false alarms being encountered. This behaviour was probably due to the distribution of the time series data not being normal. The haphazard behaviour reduces the ability of the method to detect changes, which is aggravated by the presence of chaos and instrumental or measurement noise. Mutual cross prediction is a very successful method of detecting dynamic changes and is quite robust against measurement noise. It did however require the training of a multilayer perceptron network and additional calculations that were time consuming. The proposed algorithm using the correlation dimension as test statistic with a moving window approach is very useful in detecting dynamic changes. It produced the best results on the systems considered in this study with quick and reliable detection of dynamic changes, even in then presence of some instrumental noise. The proposed method with the correlation dimension as test statistic was the only method applied to the real time series data. Here the method was successful in distinguishing between two different corrosion phenomena. The proposed method with the correlation dimension as test statistic appears to be a promising approach to the detection of dynamic change in nonlinear systems.

Hybrid systems combine the continuous behavior evolution specified by differential equations with discontinuous changes specified by discrete event logic. Usually these systems in the processing industry can be identified as having to depend on discrete decisions regarding their operation. In process control there therefore is a challenge to automate these decisions. A model predictive control (MPC) strategy was proposed and verified for the control of hybrid systems. More specifically, the dynamic matrix control (DMC) framework commonly used in industry for the control of continuous variables was modified to deal with mixed integer variables, which are necessary for the modelling and control of hybrid systems. The algorithm was designed and commissioned in a closed control loop comprising a SCADA system and an optimiser (GAMS). GAMS (General Algebraic Modelling System) is an optimisation package that is able to solve for integer/continuous variables given a model of the system and an appropriate objective function. Online and offline closed loop tests were undertaken on a benchmark interacting tank system and a heating/cooling circuit. The algorithm was also applied to an industrial problem requiring the optimal sequencing of coal locks in real time. To complete the research concerning controller design for hybrid behavior, an investigation was undertaken regarding systems that have different modes of operation due to physicochemical (inherent) discontinuities e.g. a tank with discontinuous cross sectional area, fitted with an overflow. The findings from the online tests and offline simulations reveal that the proposed algorithm, with some system specific modification, was able to control each of the four hybrid systems under investigation. Based on which hybrid system was being controlled, by modifying the DMC algorithm to include integer variables, the mixed integer predictive controller (MIPC) was employed to initiate selections, switchings and determine sequences. Control of the interacting tank system was focused on an optimum selection in terms of operating positions for process inputs. The algorithm was shown to retain the usual features of DMC (i.e. tuning and dealing with multivariable interaction). For a system with multiple modes of operation i.e. the heating/cooling circuit, the algorithm was able to switch the mode of operation in order to meet operating objectives. The MPC strategy was used to good effect when getting the algorithm to sequence the operation of several coal locks. In this instance, the controller maintained system variables within certain operating constraints. Furthermore, soft constraints were proposed and used to promote operation close to operating constraints without the danger of computational failure due to constraint violations. For systems with inherent discontinuities, a MPC strategy was proposed that predicted trajectories which crossed discontinuities. Convolution models were found to be inappropriate in this instance and state space equations describing the dynamics of the system were used instead. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2002.

Dynamic Matrix Control (DMC) has proven to be a powerful tool for optimal regulation of chemical processes under constrained conditions. The internal model of this predictive controller is based on step response measurements at an average operating point. As the process moves away from this point, however, control becomes sub-optimal due to process non-linearity. If DMC is made adaptive, it can be expected to perform well even in the presence of uncertainties, non-linearities and time-vary ing process parameters. This project examines modelling and control issues for a complex multivariable industrial operator training plant, and develops and applies a method for adapting the controller on-line to account for non-linearity. A two-input/two-output sub-system of the Training Plant was considered. A special technique had to be developed to deal with the integrating nature of this system - that is, its production of ramp outputs for step inputs. The project included the commissioning of the process equipment and the addition of instrumentation and interfacing to a SCADA system which has been developed in the School of Chemical Engineering. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2001.

This thesis presents a novel approach to the manufacture of thin film resistors using a new low resistivity material of copper, aluminium and molybdenum, which under industrially achievable optimised process conditions, is shown to be capable of producing excellent temperature coefficient of resistance (TCR) and long term stability properties. Previous developments in the field of thin film resistors have mainly centred around the well established resistive materials such as nickel-chromium, tantalum-nitride and chromium-silicon-monoxide. However recent market demands for lower value resistors have been difficult to satisfy with these materials due to their inherent high resistivity properties. This work focuses on the development and processing of a thin film resistor material system having lower resistivity and equal performance characteristics to that of the well established materials. An in depth review of thin film resistor materials and manufacturing processes was undertaken before the electrical properties of a binary thin film system of copper and aluminium were assessed. These properties were further enhanced through the incorporation of a third doping element, molybdenum, which was used to reduce the TCR and improve the electrical stability of the film. Once the desired chemical composition was established, the performance of the film was then fine tuned through optimisation of critical manufacturing process stages such as sputter deposition, heat treatment and laser adjustment. The results of these investigations were then analysed and used to generate a set of optimum process conditions, suitable for repeatedly producing thin film resistors in the 1 to 10Ω resistance range, to tolerances of less than ±0.25% and TCR values better than ±15ppm/oC.

The present research explores numerically and experimentally the process of melting and solidification of Phase Change Materials (PCM) in a latent heat thermal energy storage system (LHTESS). Further, the study will investigate various methods of intensification of heat transfer in such materials by means of metallic fins, filling particles or nanoparticles and by choosing the optimal system geometry for a rapid development of free convection flows during the melting process. The study includes three main parts. First, 3D CFD modelling was performed for the melting performance of a shell-and-tube thermal storage system with n-Octadecane as a PCM. The predicted model was in very good agreement with experimental data published in open literature. A series of numerical calculations were then undertaken to investigate the effect of nanoparticles on the heat transfer process. Dimensionless heat transfer correlations were derived for the system with Pure PCM and PCM mixed with nano-particles. In the second part of this study the experimental studies were carried out in order to investigate the performance of the laboratory thermal storage system with paraffin as the PCM. The thermal storage system was connected to evacuated tube solar collectors and its performance was evaluated in various conditions. 3D CFD model of the system was developed and numerical simulations were run for constant heat source conditions. Computational results were compared with experimental data obtained on the test rig at Northumbria University. Comparison revealed that the developed CFD model is capable to describe process of heat transfer in the system with high accuracy and therefore can be used with high confidence for modelling further cases. Finally, 3D CFD model was developed to predict the transient behaviour of a latent heat thermal energy storage system (LHTESS) in the form of a rectangular container with a central horizontal pipe surrounded by paraffin as PCM (melting temperature is 60 oC). Water was used as a heat transfer fluid (HTF). The enhancement of heat transfer in specific geometries by using external longitudinal fins on the tube and metallic porous matrix were numerically investigated. The influence of the number of fins and porosity of the matrix on the temperature distribution, melting process, melting time and natural convection phenomena were studied. Dimensionless heat transfer correlations were derived for calculation of the Nusselt number as function of Fourier, Stefan and Rayleigh numbers. These correlations to be used in the further designing process of similar thermal storage units at Northumbria University.

Process control is essential in chemical engineering and has diverse applications in automation, manufacturing, scheduling, etc. In this cross-disciplinary work, we shift the domain focus from the control of machines to the control of multiple intelligent agents. Our goal is to improve the optimization problem-solving process, such as optimal regulation of emerging technologies, in a multi-agent system. Achieving that improvement would have potential value both within and outside the chemical engineering community. This work also illustrates the possibility of applying process systems engineering techniques, especially process control, beyond chemical plants. It is very common to observe crowds of individuals solving similar problems with similar information in a largely independent manner. We argue here that the crowds can become more efficient and robust problem-solvers, by partially following the average opinion. This observation runs counter to the widely accepted claim that the wisdom of crowds deteriorates with social influence. The key difference is that individuals are self-interested and hence will reject feedbacks that do not improve their performance. We propose a multi-agent control-theoretic methodology, soft regulation, to model the collective dynamics and compute the degree of social influence, i.e., the level to which one accepts the population feedback, that optimizes the problem-solving performance. Soft regulation is a modeling language for multi-agent sociotechnical systems. The state-space formulation captures the individual learning process (i.e., open loop dynamics) as well as the influence of the population feedback in a straightforward manner. It can model a diverse set of existing multi-agent dynamics. Through numerical analysis and linear algebra, we attempt to understand the role of feedback in multi-agent collective dynamics, thus achieving multi-agent control in sociotechnical systems. Our analysis through mathematical proofs, simulations, and a human subject experiment suggests that intelligent individuals, solving the same problem (or similar problems), could do much better by adaptively adjusting their decisions towards the population average. We even discover that the crowd of human subjects could self-organize into a near-optimal setting. This discovery suggests a new coordination mechanism for enhancing individual decision-making. Potential applications include mobile health, urban planning, and policymaking.

<p>When cylinders for motor saws are manufactured there are high demands on the cylinders strength and wearing qualities. Aluminum is a material with low density and is used by Husqvarna AB for their motor saw cylinders. The aluminum is strengthening with nickel that is attached to the cylinders through electrolysis. When aluminum is in contact with oxygen a film of oxide is formed. To eliminate the oxide and to prevent formation of new oxide zinc is used. The cylinders are dipped into a bath of zinc before they pass on to a pre-nickel process, which purpose is to remove the excess of zinc so the “real” nickel process won’t be contaminated. In the process there is a selective bath where zinc is precipitated on sheet-metals through electrolysis. The problem is that while zinc is precipitated so is nickel and in a much greater extend, about 1 % zinc and 99 % nickel.</p><p>Husqvarna AB needs a technique to increase precipitation of zinc and reduce the precipitation of nickel. A small copy of the selective bath was constructed where adjustments of different parameters were possible. In the bath there was an anode of nickel, a cathode, process liquid, a pump for stirring and a plant installation from an aquarium for the temperature. In the bath different voltages, distance between anode – cathode and different sheets of metal were tested. Every test were in progress for about three days and then pieces of sheet-metals were cut and sent for analysis of zinc/nickel percentage.</p><p>A couple of the tested sheet-metals didn’t work and the ones that did work showed no special difference in zinc/nickel percentage. An increase in distance between the anode and cathode showed a small difference but not much. The alteration that showed to be most effective was to decrease the voltage. The normal voltage is 2, 8 V but when it was decreased to 2, 0 V it gave a much better result. The layer on the sheet-metal showed to contain 12 % zinc and 88 % nickel. A couple of other tests were performed with decreased voltage but no one gave as good result as 2, 0 V. If Husqvarna AB shall be able to use a lower voltage they need a greater cathode surface then they have today. That is because of the lower reaction rate. The lower reaction rate conducts an increasing amount of zinc in the bath and an electrolysis that doesn’t work completely.</p><p>Other methods for precipitation could have been tested e.g. change of anode, precipitation of zinc as a salt or a powder that could have been filtrated or a process that reduces the excess of zinc. To test any of these methods big changes in the process structure would be needed which costs both time and money. The authors therefore concluded that the best thing for Husqvarna AB to do is to increase the cathode surface by connecting another bath next to the existent.</p> / <p>Vid tillverkning av motorsågscylindrar ställs det höga krav på att de är tåliga och slitstarka. På Husqvarna AB tillverkas motorsågscylindrar av aluminium som är ett material med låg densitet. Aluminiumet förstärks med nickel som genom elektrolys fästs på cylindrarna. På aluminium bildas ett oxidskikt i kontakt med syre och för att eliminera skiktet samt undvika bildning av ny oxid så doppas cylindrarna i zink innan elektrolysen. Cylindrarna doppas hela i ett zinkatbad innan de går vidare till en förförnicklingsprocess som bland annat är till för att bli av med överflödigt zink så att det inte förorenar det ”riktiga” nickelbadet. I processen finns ett selektivt bad där zink fälls ut genom elektrolys på plåtar. Problemet är att samtidigt som zink fälls ut så fälls också nickel ut och i större mängd, ca 1 % zink respektive 99 % nickel.</p><p>Husqvarna AB behöver en teknik som ökar utfällning av zink samtidigt som nickelutfällning reduceras. En liten kopia konstruerades av det selektiva badet där justering av olika parametrar kunde utföras. I badet användes en plåtkatod och en nickelanod, vätska från processen, en pump för omrörning och ett akvarieaggregat för värme. I testbaljan provades ändringar av spänning, avstånd anod – katod och olika material som katod. Varje bad kördes ca tre dagar och sedan klipptes bitar av plåtarna bort och skickades iväg för analys av zink/nickelhalt.</p><p>Det visade sig att ett par av de testade plåtarna inte fungerade och de som fungerade visade ingen större skillnad i zink/nickelhalt. En ökning av avstånd mellan katod och anod gav en liten procentskillnad men inte tillräcklig. Den parameterändring som visade sig vara mest effektiv var att sänka spänningen. Normalt ligger spänningen på 2,8 V men när den sänktes till 2,0 V gav det bättre resultat. Skiktet på plåten visade sig nu innehålla ca 12 % zink och 88 % nickel. Ytterligare försök gjordes med lägre spänning men ingen gav lika bra resultat som 2,0 V. För att Husqvarna AB ska kunna använda lägre spänning kräver det en ökad katodyta än vad som finns i dagsläget, på grund av en lägre reaktionshastighet. Lägre reaktionshastighet leder till en för snabb ökning av zink i badet och en dåligt fungerande elektrolys.</p><p>Andra metoder för utfällning hade kunnat provats t ex byte av anod, fälla ut zink som ett salt eller pulver som sedan filtrerats bort eller en process för att minska överflödet av zink. Alla dessa metoder kräver att Husqvarna AB gör en omfattande omstrukturering av processen vilket kräver både tid och pengar. Därför kom författarna fram till att en ökad katodyta i form av ett nytt bad anslutet till det gamla vore den bästa lösningen för Husqvarna AB.</p>

The demands on Hydrogen fuel based technologies is ever increasing for substitution or replacing fossil fuel due to superior energy sustainability, national security and reduced greenhouse gas emissions. Currently, the polymer based proton exchange membrane fuel cell (PEMFC), is strongly considered for on-board hydrogen storage vehicles due to low temperature operation, efficiency and low environmental impact. However, the realization of PEMFC vehicles must overcome the portable hydrogen storage barrier. DOE and FreedomCAR technical hydrogen storage targets for the case of solid state hydrides are: (1) volumetric hydrogen density > 0.045 kgH2/L, (2) gravimetric hydrogen density > 6.0 wt%, (3) operating temperature < 150 degrees C, (4) lifetimes of 1000 cycles, and (5) a fast rate of H2 absorption and desorption. To meet these targets, we have focused on lithium borohydride systems; an alkali metal complex hydride with a high theoretical hydrogen capacity of 18 wt.%. It has been shown by Vajo et al. that adding MgH2, improves the cycling capacity of LiBH4. The pressure-composition-isotherms of the destabilized LiBH4 + MgH2 system show an extended plateau pressure around 4-5 bars at 350 degrees C with a good cyclic stability. The mentioned destabilizing mechanism was successfully utilized to synthesize the complex hydride mixture LiBH4 + 1/2MgH2 + Xmol% ZnCl2 catalyst (X=2, 4, 6, 8 and 10) by ball milling process. The added ZnCl2 exhibited some mild catalytic activity which resulted in a decomposition temperature reduction to 270 degrees C. X-ray powder diffraction profiles exhibit LiCl peaks whose intensity increases proportionately with increasing ZnCl2 indicating an interaction between catalyst and hydride system, possibly affecting the total weight percent of desorbed hydrogen. Thermal gravimetric analysis profiles for MgH2 + 5mol% nanoNi and LiBH4 + ZnCl2 + 3mol% nanoNi indicate that small concentrations of nano-nickel acts as an effective catalyst that reduces the mixture desorption temperature to around 225 degrees C and 88 degrees C, respectively. Future work will be focused on thermodynamic equilibrium studies (PCT) on the destabilized complex hydrides.