Hybrid neural networks models for a membrane reactor

Artificial neural networks (ANN) have become an established discipline and have gained extensive interest within chemical engineering. In recent years, research effort has focused on the use of hybrid artificial neural networks (HANN) models that combine both the deterministic and the ANN elements. Several methods have been proposed for combining ANN with first principle relations. In this thesis, a new hybrid scheme, which is similar to that developed by Kasprow for a space-independent and time-dependent fed-batch microbial reactor, was developed for a space-dependent steady-state enzymatic reactor. This scheme combines ANN with mass balances and assumed rate expressions. It was shown that this new hybrid scheme performed significantly better than both black-box ANN model and the hybrid ANN with only mass balance equations. An enzymatic tubular membrane reactor (TMR) was selected as a case study due to the availability of a reliable deterministic/computational model, which can provide simulated process data as needed, as well as its potential industrial importance. Also, two modeling schemes were developed, a fully \u27black box\u27 model (BANN), based on ANN technique only, and a simple hybrid model, combining ANN with mass balances (HANN1). Qualitative and quantitative comparisons of the predicted profiles of the above three modeling schemes indicated that the new hybrid scheme (HANN2) performed better than the other two schemes. As a result of adding biochemical knowledge, in the form of mass balances and simplified rate expressions, the new hybrid scheme allowed the process data to be interpolated and extrapolated more accurately

Optimization of sequential purification of beta-glucosidase from tricoderma reesei in aqueous two-phase system

A novel sequential technique was developed for the purification of a valuable enzyme, beta-glucosidase, from microorganism Tricoderma reesei. The fungus T. reesei produces cellulose degrading enzymes, called cellulases: beta-glucosidase, endo-glucanase and exo-glucanase and low molecular weight proteins. For specific applications, the enzyme must be separated from other contaminants. The sequential technique, that included affinity precipitation with chitosan followed by separation with an aqueous two-phase system (ATPS), was implemented for the purification of beta-glucosidase from the culture filtrate of T. reesei. The cultivation medium (nutrient) was optimized for the production of betaglucosidase from T. reesei cell culture. Treatment of the crude extract of T. reesei with chitosan resulted in the precipitation of endo and exo-glucanases. During this separation step, beta-glucosidase activity was completely recovered in the supematant. The enzyme was further purified from other proteins by partitioning in aqueous two-phase systems. Preliminary investigation with pure beta-glucosidase showed that the ATPS composed of PEG 4000, Potassium Phosphate salt and water is the best system for extracting the enzyme. The influences of system conditions, such as system pH and temperature, on the partition coefficients of beta-glucosidase and total proteins were evaluated in order to determine the most favorable condition for the purification of the enzyme from the culture filtrate. For the range of pH (6.0-7.5) and temperature (25-55 0C) studied, a positive correlation was obtained between these two variables and the partition coefficients. The development of reliable tools, that can predict equilibrium phase compositions and the partitioning behavior of the system components, is critical for protein purification in ATPS. Artificial Neural-Network models (ANN) offered a remarkable performance to predict equilibrium phase compositions and beta-glucosidase partition coefficients. In addition, the pilot plant study with the culture filtrate was carried out in a continuous two-stage counter-current aqueous two-phase extractor system. The pilot plant experiments demonstrated the feasibility of the continuous counter current extraction process of ATPS for large-scale purification of beta-glucosidase

Hydrolysis of lactose: estimation of kinetic parameters using Artificial Neural Networks

The analysis of any kinetic process involves the development of a mathematical model with predictive purposes. Generally, those models have characteristic parameters that should be estimated experimentally. A typical example is Michaelis-Menten model for enzymatic hydrolysis. Even though conventional kinetic models are very useful, they are only valid under certain experimental conditions. Besides, frequently large standard errors of estimated parameters are found due to the error of experimental determinations and/or insufficient number of assays. In this work, we developed an artificial neural network (ANN) to predict the performance of enzyme reactors at various operational conditions. The net was trained with experimental data obtained under different hydrolysis conditions of lactose solutions or cheese whey and different initial concentrations of enzymes or substrates. In all the experiments, commercial beta-galactosidase either free or immobilized in a chitosan support was used. The neural network developed in this study had an average absolute relative error of less than 5% even using few experimental data, which suggests that this tool provides an accurate prediction method for lactose hydrolysisFil: Cuellas, Anahí V.. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Área Ingeniería en Alimentos; ArgentinaFil: Oddone, Sebastián. Universidad Argentina de la Empresa. Facultad de Ingeniería y Ciencias Exactas; ArgentinaFil: Mammarella, Enrique José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Rubiolo, Amelia Catalina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentin

Artificial Neural Network Model Prediction of Glucose by Enzymatic Hydrolysis of Rice Straw

The aim of this paper is to predict the production of glucose using artificial neural network (ANN) and validation with the experimental values for hydrolysis process. The ANN consists of three layers which are input, hidden and output layer. The input layer is the manipulated variables in the case study, which are the activity of added cellulose, substrate initial concentration and hydrolysis time on the production of glucose while the output layer is the concentration of glucose. The performances of the model were evaluated using the coefficient of determination, mean square error and average relative deviation. The predictive model shows a good result as the coefficient of determination, 0.8361 was obtained with a small value of mean square error, 0.1947 and 5.644 as the average relative deviation. It clearly shows that ANN gives a good prediction on the enzymatic hydrolysis for the production of glucos

A critical review of machine learning for lignocellulosic ethanol production via fermentation route

In this work, machine learning (ML) applications in lignocellulosic bioethanol production were reviewed. First, the pretreatment-hydrolysis-fermentation route, the most commonly studied alternative, was summarized. Next, a bibliometric analysis was performed to identify the current trends in the field; it was found that ML applications in the field are not only increasing but also expanding their relative share in publications, with bioethanol seeming to be the most frequently researched topic while biochar and biogas are also receiving increased attention in recent years. Then, the implementation of ML for lignocellulosic bioethanol production via this route was reviewed in depth. It was observed that artificial neural network (ANN) is the most commonly used algorithm (appeared in almost 90% of articles), followed by response surface methodology (RSM) (in about 25% of articles) and random forest (RF) (in about 10% of articles). Bioethanol concentration is the most common output variable in the fermentation step, while fermentable sugar and glucose concentration are studied most in hydrolysis. The datasets are usually small, while the fitnesses of the models (R2) are usually high in the papers reviewed. Finally, a perspective for future studies, mostly considering improving data availability, was provided

The potential of waste sorghum (sorghum bicolor) leaves for bioethanol process development using Saccharomyces cerevisiae BY4743.

Masters Degree, University of KwaZulu-Natal, Pietermaritzburg.The limitations of first generation biofuels have prompted the quest for alternative energy sources. Approximately 60 million tonnes of sorghum are generated each year, with 90% being lignocellulosic waste, which is an ideal feedstock for biofuel production. The recalcitrance of lignocellulose often demands harsh pre-treatment conditions and results in the generation of fermentation inhibitors, negatively impacting process yields and economics. In this study, an artificially intelligent model to predict the profile of reducing sugars and all major volatile compounds from microwave assisted chemical pre-treatment of waste sorghum leaves (SL) was developed and validated. The pre-treated substrate was assessed for bioethanol production using Saccharomyces cerevisiae. Monod and modified Gompertz models were generated and the kinetic coefficients were compared with previous studies on different substrates. To develop the Artificial Neural Network (ANN) model, a total of 58 pre-treatment process conditions with varying parameters were experimentally assessed for reducing sugar (RS) and volatile compound production. The pre-treatment input variables consisted of acid concentration, alkali concentration, microwave duration, microwave intensity and solid-to-liquid ratio (S:L). Response Surface Methodology (RSM) was used to optimise RS production from microwave assisted acid pre-treatment of sorghum leaves, giving a coefficient of determination (R2 ) of 0.76, resulting in an optimal yield of 2.74 g RS/g SL. A multilayer perceptron ANN model was used, with a topology of 5-13-13-21. The model was trained using the backpropagation algorithm to minimise the net error value on validation. The model was validated on experimental data and R2 values of up to 0.93 were obtained. The developed model was used to predict the profile of inhibitory compounds under various pre-treatment conditions. Some of these inhibitory compounds were: acetic acid (0-186.26 ng/g SL), furfural (0-240.80 ng/g SL), 5-hydroxy methyl furfural (HMF) (0-19.20 ng/g SL) and phenol (0-7.76 ng/g SL). The developed ANN model was further subjected to knowledge extraction. Findings revealed that furfural and phenol generation during substrate pre-treatment exhibited high sensitivity to acid- and alkali concentration and S:L ratio, while phenol production showed high sensitivity to microwave duration and intensity. Furfural generation during pre-treatment of waste SL was majorly dependent on acid concentration and fit a dosage-response relationship model with a 2.5% HCl threshold. VI The pre-treated sorghum leaves were enzymatically hydrolysed and subsequently assessed for yeast growth and bioethanol production using Saccharomyces cerevisiae BY4743. Kinetic modelling was carried out using the Monod and the modified Gompertz models. Fermentations were carried out with varied initial substrate concentrations (12.5-30.0 g/L). The Monod model fitted well to the experimental data, exhibiting an R2 of 0.95. The model coefficients of maximum specific growth rate (μmax) and Monod constant (Ks) were 0.176 h-1 and 10.11 g/L respectively. Bioethanol production data fitted the modified Gompertz model with an R2 of 0.98. A bioethanol production lag time of 6.31 hours, maximum ethanol production rate of 0.52 g/L/h and a maximum potential bioethanol concentration of 17.15 g/L were obtained. These findings demonstrated that waste SL, commonly considered as post-harvest waste, contain sufficient fermentable sugar which can be recovered from appropriate HCl-based pre-treatment, for use as a low cost energy source for biofuel production. The extracted knowledge from the developed ANN model revealed significant non-linearities between the pre-treatment input conditions and generation of volatile compounds from waste SL. This predictive tool reduces analytical costs in bioprocess development through virtual analytical instrumentation. Monod and modified Gompertz coefficients demonstrated the potential of utilising sorghum leaves for bioethanol production, by providing data for early stage knowledge of the production efficiency of bioethanol production from waste SL. The generated kinetic knowledge of S. cerevisiae growth on waste SL and bioethanol formation in this study is of high importance for process optimisation and scale up towards the commercialisation of this fuel.Only available in English

Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw

In this thesis, enzymatic hydrolysis was utilized in the production of xylose from rice straw. The process model was developed by the modeling techniques using feed-forward artificial neural network (FANN) and optimized using both particle swarm optimization (PSO) and genetic algorithm (GA). The parameters studied such as temperature, agitation speed and concentration of enzyme in the process were investigated in order to get an optimum yield of xylose during enzymatic hydrolysis process. Data collected from an experimental design using response surface methodology (RSM) were used to develop the FANN modeling. The data samples has been split into training, testing and validation data set before re-sampling with bootstrap re-sampling method. Then, the FANN model was used to predict the model performance with one hidden layer and the PSO and GA were used to predict the optimum conditions of the process. The number of nodes in the hidden layer obtained is six where the performance on the model is satisfactory with the architecture of FANN, 3-6-1. The correlation coefficient of training and testing set were indicated at 0.9970 and 0.9975 respectively though the correlation coefficient of validation obtained was 0.8501. The optimization of xylose production using the GA method obtained conditions of 50.3˚C, 154 rpm and 1.6944 g/l. The optimum xylose production was predicted as 0.1845 g/l at optimal condition obtained by using GA. Meanwhile with PSO, the optimum temperature observed was at 50 °C, 132 xviii rpm for optimum value of agitation speed and 1.6474 g/l optimum xylanase concentration respectively. The optimal yield of xylose predicted was 0.1845 g/l using PSO for the enzymatic hydrolysis process. The laboratory experiment was carried out to validate the prediction of optimization result. It is shown from the experiment that the concentration of xylose obtained by using prediction optimum parameters for both PSO and GA are 0.2331 g/l and 0.2398 g/l respectively. The average error for the prediction and experimental values for the optimization are 29.97% and 26.34% for GA and PSO respectively. Therefore, the enzymatic hydrolysis on the production of xylose has been enhanced by predicting the optimum conditions utilizing the developed model that fits the experimental data