An alternative update formula for non-linear model-based iterative learning control

The conventional iterative learning control (ILC) algorithm for modelbased ILC of non-linear systems is presented with the use of a nonlinear inverse model as ILC compensator. The non-linear inverse model is solved with stable inversion. In addition, an alternative ILC algorithm for model-based ILC of non-linear systems is developed, also with using a non-linear inverse model as ILC compensator. Some connections between the conventional and alternative ILC algorithms and Picard, Mann and Ishikawa iterations are explored. The conventional and alternative algorithms are compared in a number of theoretical examples.The National Research Foundation and Investmech, Pty. Ltd.http://www.tandfonline.com/loi/gipe202017-05-31hb2017Mechanical and Aeronautical Engineerin

Structure-activity relationships of the antimicrobial peptide arasin 1 - and mode of action studies of the N terminal, proline-rich region

Arasin 1 is a 37 amino acid long proline-rich antimicrobial peptide isolated from the spider crab, Hyas araneus. In this work the active region of arasin 1 was identified through structure-activity studies using different peptide fragments derived from the arasin 1 sequence. The pharmacophore was found to be located in the proline/arginine-rich NH2 terminus of the peptide and the fragment arasin 1(1–23) was almost equally active to the full length peptide. Arasin 1 and its active fragment arasin 1(1–23) were shown to be non-toxic to human red blood cells and arasin 1(1–23) was able to bind chitin, a component of fungal cell walls and the crustacean shell. The mode of action of the fully active N-terminal arasin 1(1–23) was explored through killing kinetic and membrane permeabilization studies. At the minimal inhibitory concentration (MIC), arasin 1(1–23) was not bactericidal and had no membrane disruptive effect. In contrast, at concentrations of 5×MIC and above it was bactericidal and interfered with membrane integrity. We conclude that arasin 1(1–23) has a different mode of action than lytic peptides, like cecropin P1. Thus, we suggest a dual mode of action for arasin 1(1–23) involving membrane disruption at peptide concentrations above MIC, and an alternative mechanism of action, possibly involving intracellular targets, at MIC

Pilot-scale valorisation of salmon peptone into polyhydroxyalkanoates by mixed microbial cultures under conditions of high ammonia concentration

publishedVersio

The development and technology transfer of an industrial machine vision system for the control of a platinum flotation plant

Thesis (M. Ing.) -- University of Stellenbosch, 1995.One copy microfiche.Full text to be digitised and attached to bibliographic record

n Ondersoek na die doeltreffendheid van die deursypelingsmetode en die spreivonkmetode by die ontleding van blaarmonsters in oplossingvorm

Proefskrif (M. Sc.) -- Universiteit van Stellenbosch, 1967.Full text to be digitised and attached to bibliographic record

Advances in iterative learning control with application to structural dynamic response reconstruction

Iterative learning control (ILC) is a repetitive control scheme that uses a learning capability to improve the tracking accuracy of a desired test system output over repeated test trials. ILC is sometimes used in response reconstruction on complex engineering structures, such as ground vehicles, for purposes of fatigue testing. The compensator that is employed in ILC in such cases is traditionally an approximate, linear inverse model of the closed-loop test system. This research presents advances in ILC, particularly with respect to its application in response reconstruction for fatigue testing purposes. The contribution of this research focuses on three aspects: the use of a nonlinear inverse model in the ILC compensator instead of a linear inverse model; the use of multiple inverse models, each one defined over a different part of the test frequency band, instead of one model that covers the entire test frequency band; and the development and use of a new type of ILC algorithm. The contributions are implemented and demonstrated on a quarter vehicle road simulator, with favorable results for the use of nonlinear inverse models and multiple inverse models. The new ILC algorithm is shown to be competitive with the conventional inverse modelbased algorithm, giving comparable to slightly worse results than the conventional ILC algorithm. In order to invert the nonlinear inverse models this research also presents advances in the stable inversion method that is used to invert such models. Keywords: Iterative learning control, response reconstruction, fatigue testing, NARX models, Kolmogorov- Gabor polynomials, system identification, stable inversion, nonlinear, discrete time, Picard iteration, Mann iteration, quarter vehicle road simulator.Thesis (PhD)--University of Pretoria, 2014.lk2014Mechanical and Aeronautical EngineeringPhDUnrestricte

A generic, semi-empirical approach to the stochastic modelling of bath-type pyrometallurgical reactors

Thesis (PhD)--University of Stellenbosch, 2004.388 leaves printed on single pages, preliminary pages i- xv and numbered pages 1-371. Includes bibliography, list of tables and figures.Digitized at 330 dpi black and white and 330 dpi color PDF format (OCR), using KODAK i 1220 PLUS scanner.ENGLISH ABSTRACT: Bath type furnaces have become an established technology for the intensive smelting, converting and refining of primary and secondary raw materials. Since these furnaces normally have large inventories, long time constants and complex metallurgies, a dynamic model-based prediction strategy is the only feasible approach to operator decision support and process control. This dissertation presents a semi-empirical approach to the stochastic modelling of bath-type pyrometallurgical reactors, which leads to a generic model type called the Equilib-ARMAX model. The modelling approach is applied to three case studies: • A nickel-copper matte converting operation using a submerged lance injection reactor • A chromite smelting operation to produce high carbon ferrochrome using a direct current (DC) plasma smelting furnace • An ilmenite smelting operation to produce high titania slag and pig iron, using a direct current (DC) plasma smelting furnace. In each case, the industrial operations were analysed with regard to the practical and technological constraints which influence the type and quality of the process data. The fundamental process phenomena associated with each operation have been analysed to ascertain which fundamental variables should be included within the overall semi-empirical approach, without sacrificing model transparency, simplicity, accuracy and calculation time. It was considered that an overly complex model would be inappropriate given that data from industrial smelting operations show significant random variance. The thermochemistry and phase equilibria associated with each operation are discussed in detail, as they become the fundamental backbone of the semi-empirical models. The equilibria have been modelled with software that uses non-ideal solutions models and Gibbs free energy minimisation to predict the phase and chemical equilibria that could be expected for a given feed recipe and operating temperature. As the thermodynamic modelling software is not stable within an industrial environment, an artificial intelligent mapping technique has been developed to map process inputs to equilibrium outputs. A multi-layer perceptron neural network has been used as the convenient mapping method to represent equilibrium. The neural networks were trained using tens of thousands of feed recipes, where the feed component ratios were varied based on a 3N factorial design. The amounts and chemistries of all equilibrium phases could be calculated with high accuracies (R2 > 0.95) in all cases. Further stochastic analysis and modelling require additional information about the property distributions associated with each measurement. The homogeneities of the furnace products (slag, alloy and flue dust) critically influence the level of confidence that one can associate with plant measurements. The homogeneities were characterised for the DC plasma arc furnaces and they were benchmarked against a submerged arc furnace. It was found that the homogeneity varied per element, with silicon and sulphur tending to show highest variations in the alloy melts. The observation that the variation in these two elements are both high can partially be attributed to the fact that SiS evaporates from the bath surface, especially in regions close to the arc attachment zone. A significant negative correlation was found between the relative standard deviation per tap (using silicon) and the degree of superheat / subcooling of the alloy, indicating that the homogeneity can be strongly influenced by the changes in rheology due to subcooling below the liquidus (which leads to the precipitation of solid phases and increases the observed melt viscosity). Mixedness or homogeneity and data uncertainty are therefore inseparably linked. The relative standard deviations associated with the homogeneity characterisation, as well as known sampling and assaying variances were used to develop reconciled material balances based on measured plant data. Material balance closure was therefore obtained within the inherent uncertainties of the plant data. Biases in the plant data were identified simultaneously with data reconciliation. Moreover, it was shown using Fast Fourier Power Spectra and statespace analysis that the data reconciliation was a good low-pass filter, as it extracted the major process trends components in the noisy data and it also improved the overall dynamic behaviour characteristics of the data. Finally systems identification techniques were used to develop dynamic transfer function models that were linear in the parameters to be estimated. These systems models were based on the reconciled plant data and equilibrium predictions. The final systems models are therefore equilibrium-autoregressive-moving-average models with exogenous variables (Equilib-ARMAX). The model parameters can be estimated recursively using a simple least squares method. The final models could dynamically predict the metallurgy of the subsequent tap 4-6 hours in advance, based on a given suite of set-points, within the inherent accuracy of the data. These models may be used to suggest the optimal operating conditions through an operator guidance system, or more simply, the models are simple enough to be used in a spreadsheet on a manager's desk.AFRIKAANSE OPSOMMING: Bad-tipe oonde is reeds 'n gevestigde tegnologie wat algemeen gebruik word vir die intensiewe smelting, omsetting en raffinering van primere en sekondere roumateriale. Aangesien hierdie oonde normaalweg groot inventarisse, lang tydkonstantes en komplekse metallurgiee het, is dinamiese, modelgebaseerde voorspelling die enigste uitvoerbare benadering tot operateur besluitnemingsteunstelsels en prosesbeheer. Hierdie proefskrif stel 'n nuwe generiese, semi-empiriese benadering voor om die bad-tipe oonde stogasties te modelleer en lei tot die sogenaamde Equilib-ARMAX model. Die modelleringsbenadering word geevalueer deur drie gevallestudies: • 'n Nikkel-koper swawelsteen omsettingsproses in 'n dompel-Ians inspuit reaktor • 'n Chromiet smeltingsproses om hoe-koolstof ferrochroom te produseer in 'n gelykstroom (GS) plasmaboogoond • 'n Ilmeniet smeltingsproses om hoe titania slak en ruyster te produseer in 'n gelykstroom (GS) plasmaboogoond. In elke geval is die industriele prosesse ontleed met betrekking tot die praktiese en tegnologiese beperkings wat die tipe en die gehalte van die prosesdata beinvloed. Die fundamentele prosesgedrag van elke proses is ontleed om te bepaal welke fundamentele veranderlikes ingesluit moet word in die semi-empiriese benadering, sonder om model deursigtigheid, eenvoud, akkuraatheid en berekeningstyd in te boet. Die ontwikkeling van oor-komplekse modelle is beskou as ongepas, gegewe dat die data van industriele smeltingsprosesse beduidende onsekerhede toon. Die termochemiese en fase-ewewigte geassosieer met elke proses word breedvoerig bespreek, aangesien dit die fundamente1e grondslag van die semi-empiriese modelle verskaf. Die ewewigte is gemodelleer met rekenaar simulasie-programmatuur wat nie-ideale oplossingsmodelle en Gibbs vrye-energie minimering gebruik om die fase en chemiese ewewigte, wat verwag kan word vir 'n gegewe toevoerresep en bedryfstemperatuur, te voorspel. Aangesien termodinamiese modelleringsprogrammatuur normaalweg nie stabiele gedrag toon in 'n intydse industriele omgewing nie, word kunsmatig intelligente projeksietegnieke gebruik om prosesinsette te projekteer na die ekwavilente ewewigsvoorspellings. 'n Multilaag perseptron neurale netwerk is gebruik as 'n eenvoudige metode om hierdie ewewigsprojeksies voor te stel. Die neurale netwerke is afgerig deur van tienduisende toevoer resepte gebruik te maak. Die verhoudings van die komponente in die voer is gewissel gebaseer op 'n 3N faktoriaalontwerp. Die hoeveelhede en samestelling van al die ewewigsfases kon in alle gevalle bereken word met hoe akkuraatheid (R2 > 0.95). Verdere stogastiese analise en modellering is slegs moontlik met kennis oor die eienskapsverspreidings geassosieer met elke komponent. Die homogeniteite van die oondprodukte (slak, legering en vlieg-as) bepaal, tot 'n groot mate, die betroubaarheidsvlak van die aanlegmetings. Homogeniteite is gekarakteriseer vir die GS-plasmaboogoonde en is vergelyk met die homogeniteite wat in dompelboogoonde gevind word. Die homogeniteite het gevarieer per komponent. Silikon en swawel neig om die grootste ruimtelike variasies te toon in die legerings wat bestudeer is. 'n Beduidende negatiewe korrelasie is gevind tussen die relatiewe standaardafwyking per tap (gebaseer op silikon) en die graad van superverhitting / onderverkoeling van die legering. Dit dui aan dat die homogeniteit sterk beinvloed word deur veranderinge in die smelt reologie. Vermenging, reologie, homogeniteit en data onsekerheid (integriteit) is daarom ten nouste gekoppel. Die relatiewe standaardafwykings geassosieer met die homogeniteitsbepaling, asook die monsternemings- en ontledingsvariansies, is gebruik om die aanlegdata te rekonsilieer onderhewig aan die behoud van die komponent en totale stroom massabalanse. Die massabalanse is dus gesluit deur aanpassings aan die metings te maak binne die inherente onsekerhede in die data. Sistematiese foute in die data is gelyktydig met die rekonsiliasie geidentifiseer. Verder is deur diskrete Fourier energiespektra en toestand-ruimte analises getoon dat massabalans-rekonsiliasie dien as 'n goeie seinfilter om hoe-frekwensie geraas te verminder en tergelykertyd die dinamiese gedragseienskappe van die data te verbeter. Stelsel-identifikasietegnieke is gebruik om dinamiese oordragsfunksiemodelle te ontwikkel wat linieer is met betrekking tot die modelparameters. Hierdie stelselmodelle is gebaseer op gerekonsilieerde data, eksogene prosesdata en ewewigsberekeninge, en word vervolgens ewewigs-autoregressiewe-lopende-gemiddelde modelle met eksogene veranderlikes (Equilib ARMAX) genoem. Die modelparameters kan deur gewone kleinste-kwadrate metodes beraam word. Die finale modelle kan die metallurgie van toekomstige tappe 4-6 uur voortydig voorspel, gebaseer op beskikbare stelpunte en binne die inherente presisie van die data. Hierdie modelle kan gebruik word om optimale bedryfskondisies vir prosesbeheer te identifiseer, en is eenvoudig genoeg om in sigbladformaat op 'n aanlegbetuurder se rekenaar gebruik te kan word

An iterative method and its application to stable inversion

In this paper, we study convergence and data dependence of SP and normal-S iterative methods for the class of almost contraction mappings under some mild conditions. The validity of these theoretical results is confirmed with numerical examples. It has been observed that a special case of SP iterative method, namely normal-S iterative method, performs better and so the latter is implemented in the stable inversion of nonlinear discrete time dynamical systems to yield convergence results when Picard iterative method diverges. This is also illustrated with a numerical example. Our work extends and improves upon many results existing in the literature.http://link.springer.com/journal/5002019-07-12hj2019Mechanical and Aeronautical Engineerin

Selective intracellular delivery of thiolated cargo to tumor and neovasculature cells using histidine-rich peptides as vectors

Short histidine-rich peptides could serve as novel activatable vectors for delivering cytotoxic payloads to tumor and neovasculature cells. This explorative study reports preliminary results showing that zinc ions, which are found in elevated levels at neovasculature sites, can trigger the intracellular delivery of a short antimicrobial peptide when conjugated to a histidine-rich peptide through a disulfide bond. The importance of exofacial thiols in the mode of action of these disulfide-linked conjugates is also shown

Tumor lysis with LTX-401 creates anticancer immunity

Local immunotherapies such as the intratumoral injection of oncolytic compounds aim at reinstating and enhancing systemic anticancer immune responses. LTX-315 is a first-in-class, clinically evaluated oncolytic peptide-based local immunotherapy that meets these criteria. Here, we show that LTX-401, yet another oncolytic compound designed for local immunotherapy, depicts a similar safety profile and that sequential local inoculation of LTX-401 was able to cure immunocompetent host from subcutaneous MCA205 and TC-1 cancers. Cured animals exhibited long-term immune memory effects that rendered them resistant to rechallenge with syngeneic tumors. Nevertheless, the local treatment with LTX-401 alone had only limited abscopal effects on secondary contralateral lesions. Anticancer effects resulting from single as well as sequential injections of LTX-401 were boosted in combination with PD-1 and CTLA-4 immune checkpoint blockade (ICB), and sequential LTX-401 treatment combined with double ICB exhibited strong abscopal antineoplastic effects on contralateral tumors underlining the potency of this combination therapy