Candida Antarctica As Catalyst For Polycaprolactone Synthesis: Effect Of Temperature And Solvents.

The Effects of temperature on ring-opening bulk polymerizations of ε-caprolactone was studied by using lipase Novozym 435 (immobilized form of lipase B from Candida antarctica), as biocatalyst

Performance comparison of feedforward neural network training algorithms in modeling for synthesis of polycaprolactone via biopolymerization

This paper reports the biopolymerization of ε-caprolactone, using lipase Novozyme 435 catalyst at varied impeller speeds and reactor temperatures. A multilayer feedforward neural network (FFNN) model with 11 different training algorithms is developed for the multivariable nonlinear biopolymerization of polycaprolactone (PCL). In previous works, biopolymerization carried out in scaled-up bioreactors is modeled through FFNN. No review discussed the role of different training algorithms in artificial neural network on the estimation of biopolymerization performance. This paper compares mean absolute error, mean square error, and mean absolute percentage error (MAPE) in the PCL biopolymerization process for 11 different training algorithms that belong to six classes, namely (1) additive momentum, (2) self-adaptive learning rate, (3) resilient backpropagation, (4) conjugate gradient backpropagation, (5) quasi-Newton, and (6) Bayesian regulation propagation. This paper aims to identify the most effective training method for biopolymerization. Results show that the quasi-Newton-based and Levenberg–Marquardt algorithms have the best performance with MAPE values of 4.512, 5.31, and 3.21% for the number of average molecular weight, weight average molecular weight, and polydispersity index, respectively

Data augmentation and machine learning techniques for control strategy development in bio-polymerization process

Machine learning has been increasingly used in biochemistry. However, in organic chemistry and other experiment-based fields, data collected from real experiments are inadequate and the current coronavirus disease (COVID-19) pandemic has made the situation even worse. Such limited data resources may result in the low performance of modeling and affect the proper development of a control strategy. This paper proposes a feasible machine learning solution to the problem of small sample size in the bio-polymerization process. To avoid overfitting, the variational auto-encoder and generative adversarial network algorithms are used for data augmentation. The random forest and artificial neural network algorithms are implemented in the modeling process. The results prove that data augmentation techniques effectively improve the performance of the regression model. Several machine learning models were compared and the experimental results show that the random forest model with data augmentation by the generative adversarial network technique achieved the best performance in predicting the molecular weight on the training set (with an R(2) of 0.94) and on the test set (with an R(2) of 0.74), and the coefficient of determination of this model was 0.74

Parametric optimization of polycaprolactone synthesis catalysed by Candida antarctica lipase B using response surface methodology

A statistical approach with D-optimal design was used to optimize the process parameters for polycaprolactone (PCL) synthesis. The variables selected were temperature (50°C-110°C), time (1-7 h), mixing speed (50-500 rpm) and monomer/solvent ratio (1:1-1:6). Molecular weight was chosen as response and was determined using matrix-assisted laser desorption/ionization time of flight (MALDI TOF). Using the D-optimal method in design of experiments, the interactions between parameters and responses were analysed and validated. The results show a good agreement with a minimum error between the actual and predicted values

Development of surrogate predictive models for the nonlinear elasto-plastic response of medium density fibreboard-based sandwich structures

Medium-density fibreboard (MDF) belongs to a class of engineered wood products facilitating efficient use of wood wastes. For this class of materials, the development of predictive models is crucial for the simulation of their responses under mechanical loads. In this study, samples of sandwich structures based on MDF as the skins and a mushroom-based foam as the core are fabricated and tested under edgewise compression tests. Results from the tests support the idea that increasing the thickness of the skins strengthens the response of the sandwich structure against buckling failure, but also revealed that thicker skins are susceptible to complex failure modes. Towards data-driven constitutive modelling of the nonlinear elastic-plastic response of this bio-based structure, predictive models premised on feedforward backpropagation neural network (FFNN), cascade-forward backpropagation neural network (CFNN), and generalized regression neural network (GRNN) were developed. Performance of the models was assessed via error criteria that include the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE). Results from the models indicate that CFNN with 15 hidden neurons under the Levenberg-Marquardt backpropagation training algorithm outperformed FFNN and GRNN models, with R2=1.0, RMSE=0.0030 and MAE=0.0019

Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates

At present, polyhydroxyalkanoates (PHAs) have been considered as a promising alternative to conventional plastics due to their diverse variability in structure and rapid biodegradation. To ensure cost competitiveness in the market, thermoseparating aqueous two-phase extraction (ATPE) with the advantages of being mild and environmental-friendly was suggested as the primary isolation and purification tool for PHAs. Utilizing two-level full factorial design, this work studied the influence and interaction between four independent variables on the partitioning behavior of PHAs. Based on the experimental results, feed forward neural network (FFNN) was used to develop an empirical model of PHAs based on the ATPE thermoseparating input-output parameter. In this case, bootstrap resampling technique was used to generate more data. At the conditions of 15 wt % phosphate salt, 18 wt % ethylene oxide–propylene oxide (EOPO), and pH 10 without the addition of NaCl, the purification and recovery of PHAs achieved a highest yield of 93.9%. Overall, the statistical analysis demonstrated that the phosphate concentration and thermoseparating polymer concentration were the most significant parameters due to their individual influence and synergistic interaction between them on all the response variables. The final results of the FFNN model showed the ability of the model to seamlessly generalize the relationship between the input–output of the process