This work investigates the use of artificial neural networks in modeling an industrial fermentation process of Pleuromutilin produced by Pleurotus mutilus in a fed-batch mode. Three feed-forward neural network models characterized by a similar structure (five neurons in the input layer, one hidden layer and one neuron in the output layer) are constructed and optimized with the aim to predict the evolution of three main bioprocess variables: biomass, substrate and product. Results show a good fit between the predicted and experimental values for each model (the root mean squared errors were 0.4624% - 0.1234 g/L and 0.0016 mg/g respectively). Furthermore, the comparison between the optimized models and the unstructured kinetic models in terms of simulation results shows that neural network models gave more significant results. These results encourage further studies to integrate the mathematical formulae extracted from these models into an industrial control loop of the process.

Artificial neural networks are applied to high-pressure vapor liquid equilibrium (VLE) related literature data to develop and validate a model capable of predicting VLE of six CO2-ester binaries (CO2-ethyl caprate, CO2-ethyl caproate, CO2-ethyl caprylate, CO2-diethyl carbonate, CO2-ethyl butyrate and CO2-isopropyl acetate). A feed forward, back propagation network is used with one hidden layer. The model has five inputs (two intensive state variables and three pure ester properties) and two outputs (two intensive state variables).The network is systematically trained with 112 data points in the temperature and pressure ranges (308.2-328.2 K), (1.665-9.218 MPa) respectively and is validated with 56 data points in the temperature range (308.2-328.2 K). Different combinations of network architecture and training algorithms are studied. The training and validation strategy is focused on the use of a validation agreement vector, determined from linear regression analysis of the plots of the predicted versus experimental outputs, as an indication of the predictive ability of the neural network model. Statistical analyses of the predictability of the optimised neural network model show excellent agreement with experimental data (a coefficient of correlation equal to 0.9995 and 0.9886, and a root mean square error equal to 0.0595 and 0.00032 for the predicted equilibrium pressure and CO2 vapor phase composition respectively). Furthermore, the comparison in terms of average absolute relative deviation between the predicted results for each binary for the whole temperature range and literature results predicted by some cubic equation of state with various mixing rules and excess Gibbs energy models shows that the artificial neural network model gives far better results.

O tratamento de 3-amino-2-metilquinazolina/6-bromo-2-metilquinazolina-4(3H)-onas, 2a,b, com 2,3-indolinediona na presença de traços de ácido acético glacial forneceu 3-{(2'-oxo-1',2'-di-hidroindol-3'-ilideno)amino} -2-metilquinazolina/6-bromo-2-metilquinazolina-4-(3H)-onas, 3a,b, as quais foram condensadas com várias aminas secundárias e formaldeído em etanol para originar compostos do tipo 3-{(1'-alquil/arilaminometil-2'-oxo-1',2'-di-hidroindol-3'-ilideno)amino} -2-metil-6-quinazolin-4-(3H)-onas, 4a1-6 e 4b1-6.Os compostos sintetizados foram caracterizados por análise elementar, espectroscopia no infravermelho, RMN ¹H e espectrometria de massas. Adicionalmente, foram investigadas suas atividades antimicrobiana, analgésica, anti-inflamatória e anti-helmíntica. Os resultados das atividades biológicas revelaram que os compostos 4a3, 4a4 e 4b6 exibiram atividades analgésica e anti-inflamatória significativas. Os compostos 4b5 e 4b6 apresentaram atividade anti-helmíntica, quando testados frente a Pheretima posthuma.

The 3-amino-2-methylquinazoline/6-bromo-2-methylquinazoline-4(3H)-ones, 2a,b, on treatment with 2,3-indolinedione in the presence of traces of glacial acetic acid yielded 3-{(2'-oxo-1',2'-dihydroindole-3'-ylidene)amino} -2-methylquinazolin/6-bromo-2-methylquinazolin-4-(3H)-ones, 3a,b, which on condensation with various secondary amines and formaldehyde in ethanol afforded title compounds 3-{(1'-alkyl/arylaminomethyl-2'-oxo-1',2'-dihydroindole-3'-ylidene)amino} -2-methyl-6-quinazolin-4-(3H)-ones, 4a1-6 and 4b1-6. C, H, N analysis, infrared spectroscopy, ¹H NMR, and mass spectroscopy allowed the identification of the synthesized compounds, which were investigated for their antimicrobial, analgesic, anti-inflammatory and antihelmintic activities. The results of the biological activities revealed that the compounds 4a3, 4a4 and 4b6 exhibited significant analgesic and anti-inflammatory activities. Compounds 4b5 and 4b6 showed antihelmintic activity when tested against Pheretima posthuma.