The results of a theoretical and experimental investigation into the drying of wheat and barley in two stages with an intervening rest period are presented. The reduction in drying time, excluding rest period, has been determined in comparison with the conventional continuous drying for various drying requirements. The effect of airflow rate and the temperature difference between grain and air on the reduction in moisture content and the time required to cool the grain during dryeration is also included. The moisture diffusion equation was solved numerically assuming a spherical grain. The variable grid spacing, Crank-Nicolson approximation technique and the Gauss-Seidel iterative procedure was employed. The theoretical predictions were compared with experimental results. The drying and resting was performed on a thin layer at a temperature of 60°C. An automatic micro-computer based system was developed to record and store the experimental data. The results indicate that the moisture redistribution during resting is well advanced after a period of two hours for wheat and one hour for barley. The extent of redistribution was measured by the increase in drying rate observed as the rest period was extended. An optimum moisture content for commencing resting is specified, which is a function of initial, final and equilibrium moisture contents. This optimum was chosen to minimise the actual drying time. There is good agreement between the theoreticaaand experimental predictions. It was found that the incorporation of a surface resistance into the diffusion model improves the description of the experimental results. The results enable a drying strategy to be specified that reduces the actual drying time by as much as 39%. - iv For dryeration experiments, the grains pre-heated to different temperatures were put into a well insulated aluminium cylinder and aerated at various airflow rates. An airflow rate of about 60-120 m3/hr/m3 of grain was found to be optimum. The moisture reduction during cooling was observed to be 0.65 to 0.78% (db) per 10°C temperature difference. It was noticed that moisture reduction also depends on initial moisture content of the grain. The practical implications of two-stage drying are discussed.

Various drying models have been developed in the extent which they are available for the analysis of drying processes in a variety of practical drying systems. However, most were focused only on a single unit operation; mainly the dryer. Nevertheless other unit operations such as cooling and tempering units are also employed in industrial drying systems. Therefore, there is an important need for an integrated analysis of rice drying systems which takes into account all the interactions between the units that appear in a drying process. The aim is to select a process out of the large number of alternatives and operating conditions which meet the specified performance. In this work, the synthesis problem of drying processes will be thoroughly investigated using various drying models. Both simplified (empirical) and rigorous (theoretical) models were used. The aim is to find the optimum configuration and operating conditions which satisfy two optimization criteria. One is to maximize the quality (head rice yield) and the other is to minimize the energy consumption. To solve the synthesis problem, mathematical programming will be used as a tool. Three major steps involving the application of mathematical programming in synthesis problems were developed and presented; superstructure representation, problem formulation and optimization strategy. For the synthesis problem using empirical models, the problem was formulated as an MINLP model. However, due to the fact that different mathematical models are often possible for the same synthesis problem and the recent advances in modeling techniques, generalized disjunctive programming (GDP), known as an alternative model to MINLP, was used. The objectives are to investigate the benefit of using GDP as an alternative model to MINLP and also to exploit a disjunction part of a GDP model for integrating alternative choices of empirical drying models to eliminate the problem of having drying models which are valid only in a small range of operating conditions. The results showed that different drying strategies were obtained from using different drying models in the case of maximum head of rice yield (quality) while the same strategies have been found from using different drying models in the case of minimum energy consumption. This finding is due to the reason that quality as an objective function is highly nonlinear; therefore it contains many local solutions while the energy objective function is a simple linear function. In the aspect of using GDP model, we found that GDP models provide good structure of variable relationships which can improve the search strategy and solution efficiency for the problem dealing with highly nonlinear functions such as in the case of maximum head price yield. Moreover, because of this good characteristic of MINLP based GDP model, the synthesis problem of rice drying processes dealing with various kinds of empirical models can be solved in reasonable time in GAMS. Nonetheless, in the case that the optimization problem is dealing with the simple mathematical function, the GDP model did not outperform the ad hoc MINLP model for the case of minimizing energy consumption. Also, GDP modeling framework facilitated the problem formulation of the synthesis problem which had two drying models valid in a different range of drying operations in rice drying processes. The synthesis problem using theoretical models arising from the simultaneous heat and mass transfer balances gave rise to a mixed-integer nonlinear programming (MIDO) model. Such problem is highly nonlinear, multimodal and discontinuous in nature and is very difficult to solve. A hybrid method which combines genetic algorithms (GAs) and control vector parameterization (CVP) approach was proposed to solve this problem. In the case of maximum head rice yield, the results of the synthesis problem showed that high quality rice grain can be preserved regardless of the choice of drying configuration as long as the drying process is operated under a condition which produces the least amount of moisture gradient within the rice grain. Many local optimum solutions which gave rise to different drying configurations and operating policies were found from using different initial guesses. In the case of minimum energy consumption, the results showed that a cooling-tempering configuration which operates at ambient temperature gave the minimum energy consumption. Different initial guesses converged to the same drying configuration (cooling-tempering) but different operating policies and total number of passes. Moreover, since the optimal operating time in a cooling unit is at the upper operating bound allowed in this unit, the effect of the bound of operating time for a cooling unit on the total number of passes required was studied. The results showed that less number of passes would be obtained if longer periods of cooling are allowed. The hybrid proposed method was able to solve MIDO problems; albeit at a relatively large computational expense. For the comparison aspect between the theoretical and empirical models for synthesis of rice drying processes, empirical models are easier to use for the synthesis problem but they are valid only within the range which they were developed. Also, there is a need for developing a model for each particular unit employed in rice drying processes. For the synthesis problem with theoretical models, this problem gives rise to the most difficult class of optimization problems; however, a theoretical model provides a better understanding of the drying kinetics happening in rice grain. Moreover, theoretical models alleviate the need to develop models for each particular unit employed in rice drying systems. The common feature found from using theoretical and empirical models is that head rice yield objective function always gives rise to different choices of drying configurations while the energy objective function always give rise to a unique drying configuration (cooling-tempering). Different drying strategies have been found from using different drying models. These alternative configurations provide a broader vision on the operation of drying systems. To decide which one is the best, other factors must be taken into account such as investment cost, term of uses and available technology.

Various drying models have been developed in the extent which they are available for the analysis of drying processes in a variety of practical drying systems. However, most were focused only on a single unit operation; mainly the dryer. Nevertheless other unit operations such as cooling and tempering units are also employed in industrial drying systems. Therefore, there is an important need for an integrated analysis of rice drying systems which takes into account all the interactions between the units that appear in a drying process. The aim is to select a process out of the large number of alternatives and operating conditions which meet the specified performance. In this work, the synthesis problem of drying processes will be thoroughly investigated using various drying models. Both simplified (empirical) and rigorous (theoretical) models were used. The aim is to find the optimum configuration and operating conditions which satisfy two optimization criteria. One is to maximize the quality (head rice yield) and the other is to minimize the energy consumption. To solve the synthesis problem, mathematical programming will be used as a tool. Three major steps involving the application of mathematical programming in synthesis problems were developed and presented; superstructure representation, problem formulation and optimization strategy. For the synthesis problem using empirical models, the problem was formulated as an MINLP model. However, due to the fact that different mathematical models are often possible for the same synthesis problem and the recent advances in modeling techniques, generalized disjunctive programming (GDP), known as an alternative model to MINLP, was used. The objectives are to investigate the benefit of using GDP as an alternative model to MINLP and also to exploit a disjunction part of a GDP model for integrating alternative choices of empirical drying models to eliminate the problem of having drying models which are valid only in a small range of operating conditions. The results showed that different drying strategies were obtained from using different drying models in the case of maximum head of rice yield (quality) while the same strategies have been found from using different drying models in the case of minimum energy consumption. This finding is due to the reason that quality as an objective function is highly nonlinear; therefore it contains many local solutions while the energy objective function is a simple linear function. In the aspect of using GDP model, we found that GDP models provide good structure of variable relationships which can improve the search strategy and solution efficiency for the problem dealing with highly nonlinear functions such as in the case of maximum head price yield. Moreover, because of this good characteristic of MINLP based GDP model, the synthesis problem of rice drying processes dealing with various kinds of empirical models can be solved in reasonable time in GAMS. Nonetheless, in the case that the optimization problem is dealing with the simple mathematical function, the GDP model did not outperform the ad hoc MINLP model for the case of minimizing energy consumption. Also, GDP modeling framework facilitated the problem formulation of the synthesis problem which had two drying models valid in a different range of drying operations in rice drying processes. The synthesis problem using theoretical models arising from the simultaneous heat and mass transfer balances gave rise to a mixed-integer nonlinear programming (MIDO) model. Such problem is highly nonlinear, multimodal and discontinuous in nature and is very difficult to solve. A hybrid method which combines genetic algorithms (GAs) and control vector parameterization (CVP) approach was proposed to solve this problem. In the case of maximum head rice yield, the results of the synthesis problem showed that high quality rice grain can be preserved regardless of the choice of drying configuration as long as the drying process is operated under a condition which produces the least amount of moisture gradient within the rice grain. Many local optimum solutions which gave rise to different drying configurations and operating policies were found from using different initial guesses. In the case of minimum energy consumption, the results showed that a cooling-tempering configuration which operates at ambient temperature gave the minimum energy consumption. Different initial guesses converged to the same drying configuration (cooling-tempering) but different operating policies and total number of passes. Moreover, since the optimal operating time in a cooling unit is at the upper operating bound allowed in this unit, the effect of the bound of operating time for a cooling unit on the total number of passes required was studied. The results showed that less number of passes would be obtained if longer periods of cooling are allowed. The hybrid proposed method was able to solve MIDO problems; albeit at a relatively large computational expense. For the comparison aspect between the theoretical and empirical models for synthesis of rice drying processes, empirical models are easier to use for the synthesis problem but they are valid only within the range which they were developed. Also, there is a need for developing a model for each particular unit employed in rice drying processes. For the synthesis problem with theoretical models, this problem gives rise to the most difficult class of optimization problems; however, a theoretical model provides a better understanding of the drying kinetics happening in rice grain. Moreover, theoretical models alleviate the need to develop models for each particular unit employed in rice drying systems. The common feature found from using theoretical and empirical models is that head rice yield objective function always gives rise to different choices of drying configurations while the energy objective function always give rise to a unique drying configuration (cooling-tempering). Different drying strategies have been found from using different drying models. These alternative configurations provide a broader vision on the operation of drying systems. To decide which one is the best, other factors must be taken into account such as investment cost, term of uses and available technology.

Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: A mineral concentrate drying process consisting of a hot gas generator, a flash dryer and a feeding section is found to be the bottleneck in the platinum concentrate smelting process. This operation is used as a case study for system identification and model-based control of dryers. Based on the availability of a month's worth of dryer data obtained from a historian, a third party modelling and control software vendor is interested in the use of this data for data driven model construction and options for dryer control. The aimed contribution of this research is to use only data driven techniques and attempt an SID experiment and use of this model in a controller found in literature to be applicable to the dryer process. No first principle model was available for simulation or interpretation of results. Data were obtained for the operation from the plant historian, reduced, cleaned and investigated for deterministic information through surrogate data comparison – resulting in usable timeseries from the plant data. The best datasets were used for modelling of the flash dryer and hot gas generator operations individually, with the hot gas generator providing usable results. The dynamic, nonlinear autoregressive models with exogenous inputs were identified by means of a genetic programming with orthogonal least squares toolbox. The timeseries were reconstructed as a latent variable set, or “pseudo-embedding”, using the delay parameters as identified by average mutual information, autocorrelation and false nearest neighbours. The latent variable reconstruction resulted in a large solution space, which need to be investigated for an unknown model structure. Genetic Programming is capable of identifying unknown structures. Freerun prediction stability and sensitivity analysis were used to assess the identified best models for use in model based control. The best two models for the hot gas generator were used in a basic model predictive controller in an attempt to only track set point changes. One step ahead modelling of the flash dryer outlet air temperature was unsuccessful with the best model obtaining a validation R2 = 43%. The lack of process information contained in the available process variables are to blame for the poor model identification. One-step ahead prediction of the hot gas generator resulted in a top model with validation R2 = 77.1%. The best two hot gas generator models were implemented in a model predictive controller constructed in a real time plant data flow simulation. This controller's performance was measured against set point tracking ability. The MPC implementation was unsuccessful due to the poor freerun prediction ability of the models. The controller was found to be unable to optimise the control moves using the model. This is assigned to poor model freerun prediction ability in one of the models and a too complex freerun model structure required. It is expected that the number of degrees of freedom in the freerun model is too much for the optimiser to handle. A successful real time simulation architecture for the plant dataflow could however be constructed in the supplied software. It is recommended that further process measurements, specifically feed moisture content, feed temperature and air humidity, be included for the flash dryer; closed loop system identification be investigated for the hot gas generator; and a simpler model structure with smaller reconstructed latent variable regressor set be used for the model predictive controller. / AFRIKAANSE OPSOMMING: 'n Drogings proses vir mineraal konsentraat bestaan uit drie eenhede: 'n lug verwarmer-, 'n blitsdroeër- en konsentraat toevoer eenheid. Hierdie droeër is geïdentifiseer as die bottelnek in die platinum konsentraat smeltingsproses. Die droeër word gebruik as 'n gevallestudie vir sisteem identifikasie asook model-gebasseerder beheer van droeërs. 'n Maand se data verkry vanaf die proses databasis, het gelei tot 'n derde party industriële sagteware en beheerstelsel maatskappy se belangstelling in data gedrewe modelering en beheer opsies vir die drogings proses. Die doelwit van hierdie studie is om data gedrewe modeleringstegnieke te gebruik en die model in 'n droeër-literatuur relevante beheerder te gebruik. Geen eerste beginsel model is beskikbaar vir simulasie of interpretasie van resultate nie. Die verkrygde data is gereduseer, skoon gemaak en bestudeer om te identifiseer of die tydreeks deterministiese inligting bevat. Dit is gedoen deur die tydreeks met stochastiese surrogaat data te vergelyk. Die mees gepaste datastelle is gebruik vir modellering van die blitsdroeër en lugverwarmer afsonderlik. Die nie-liniêre, dinamiese nie-linieêre outeregressie modelle met eksogene insette was deur 'n genetiese programmering algoritme, met ortogonale minimum kwadrate, identifiseer. Die betrokke tydreeks is omskep in 'n hulp-veranderlike stel deur gebruik te maak van vertragings-parameters wat deur gemiddelde gemeenskaplike inligting, outokorrelasie en vals naaste buurman metodes verkry is. Die GP algoritme is daartoe in staat om the groot oplossings ruimte wat deur hierdie hulp-veranderlike rekonstruksie geskep word, te bestudeer vir 'n onbekende model struktuur. Die vrye vooruitskattings vermoë, asook die model sensitiwiteit is inag geneem tydens die analiese van die resultate. Die beste modelle se gepastheid tot model voorspellende beheer is gemeet deur die uitkomste van 'n sensitiwiteits analise, asook 'n vrylopende voorspelling, in oënskou te neem. Die een-stap vooruit voorspellende model van die droeër was onsusksesvol met die beste model wat slegs 'n validasie R2 = 43% kon behaal. Die gebrekkige meet instrumente in die droeër is te blameer vir die swak resultate. Die een-stap vooruit voorspellende model van die lug verwarmer wat die beste gevaar het, het 'n validasie R2 = 77.1% gehad. 'n Basiese model voorspellende beheerder is gebou deur die 2 beste modelle van slegs die lugverwarmer te gebruik in 'n intydse simulasie van die raffinadery data vloei struktuur. Hierdie beheerder se vermoë om toepaslike beheer uit te oefen, is gemeet deur die slegs die stelpunt te verander. Die beheerder was egter nie daartoe in staat om die insette te optimeer, en so die stelpunt te volg nie. Hierdie onvermoë is as gevolg van die kompleks vrylopende model struktuur wat oor die voorspellingsvenster optimeer moet word, asook die onstabiele vryvooruitspellings vermoë van die modelle. Die vermoede is dat die loslopende voorspelling te veel vryheids grade het om die insette maklik genoeg te optimeer. Die intydse simulasie van die raffinadery se datavloei struktuur was egter suksesvol. Beter meting van noodsaaklike veranderlikes vir die droër, o.a. voginhoud van die voer, voer temperatuur, asook lug humiditeit; geslotelus sisteem identifikasie vir die lugverwarmer; asook meer eenvoudige model struktuur vir gebruik in voorspellende beheer moontlik vermag deur 'n kleiner hulp veranderlike rekonstruksie te gebruik.

Cette étude porte sur l’influence de différents traitements sur les caractéristiques et l’évolution de la matière organique de boues de stations d’épuration (Step). Les échantillons étudiés proviennent de 3 stations municipales de la Vienne. Le premier volet développé au cours de l’étude a pour but la mise au point d’une méthode d’analyse qualitative et quantitative des constituants des boues de Step. Les résultats obtenus démontrent le potentiel de la thermochimiolyse-GCMS à caractériser, sans extraction préalable, la matière organique d’un échantillon brut. Le deuxième volet de cette thèse s’est intéressé à l’influence de différents traitements (séchages, accélérateur d’électrons et méthanisation) sur l’évolution de la matière organique. Ainsi, le séchage thermique provoque une fragilisation de la matière organique, le séchage solaire une complexification et les lits plantés de roseaux n’ont pas d’influence significative à court terme. L’application du procédé d’oxydation avancé sur une boue flottée a provoqué une acidification et des changements structuraux de la matière organique. Ainsi une faible dose (1,25 kGy) a conduit à une complexification de la matière organique alors qu’une plus forte dose (50 kGy) semble la fragiliser. La modification de la matière organique à l’issue du procédé de méthanisation concerne uniquement la fraction lipidique et notamment des acides gras. / This study investigated the influence of different treatments on the characteristics and evolution of organic matter of municipal wastewater sludge. For this, three municipal wastewater treatment plants of Vienne department were sampled.The first phase developed during the study focused on the improvement of a method for qualitative and quantitative analysis of the sewage sludge’s constituents. The presented results demonstrate the strong potential of the thermochemolysis-GCMS to characterize, without extraction, the organic matter of a raw sample.The second part of this Ph.D has focused on the influence of various treatments (drying, electron beam and methanation) on the evolution of organic matter. Thus, the thermal drying causes weakening of the organic matter while the solar drying induces its complexification and reed beds induce no significant change. The application of an advanced oxidation process on a floated sludge caused acidification and structural changes of the organic matter. A low dose (1.25 kGy) led to more complex organic matter, while a higher dose (50 kGy) seems to weaken it. The changes in organic matter induced by anaerobic digestion mainly concerned the lipid fraction, in particular fatty acids.