RESUMEN
Renewable sources like biofuels have gained significant attention to meet the rising demands of energy supply. Biofuels find useful in several domains of energy generation such as electricity, power, or transportation. Due to the environmental benefits of biofuel, it has gained significant attention in the automotive fuel market. Since the handiness of biofuels become essential, effective models are required to handle and predict the biofuel production in realtime. Deep learning techniques have become a significant technique to model and optimize bioprocesses. In this view, this study designs a new optimal Elman Recurrent Neural Network (OERNN) based prediction model for biofuel prediction, called OERNN-BPP. The OERNN-BPP technique pre-processes the raw data by the use of empirical mode decomposition and fine to coarse reconstruction model. In addition, ERNN model is applied to predict the productivity of biofuel. In order to improve the predictive performance of the ERNN model, a hyperparameter optimization process takes place using political optimizer (PO). The PO is used to optimally select the hyper parameters of the ERNN such as learning rate, batch size, momentum, and weight decay. On the benchmark dataset, a sizable number of simulations are run, and the outcomes are examined from several angles. The simulation results demonstrated the suggested model's advantage over more current methods for estimating the output of biofuels.
RESUMEN
A novel, economic, and time-efficient stability-indicating, reverse-phase ultra-performance liquid chromatographic (RP-UPLC) method has been developed for the analysis of verapamil hydrochloride in the presence of both impurities and degradation products generated by forced degradation. When verapamil hydrochloride was subjected to acid hydrolysis, oxidative, base hydrolysis, photolytic, and thermal stress, degradation was observed only in oxidative and base hydrolysis. The drug was found to be stable to other stress conditions. Successful chromatographic separation of the drug from impurities formed during synthesis and from degradation products formed under stress conditions was achieved on a Shimpak XR ODS, 75mm×3.0mm, 1.7µ particle size column, UV detection at 278nm and a gradient elution of ammonium formate, orthophosphoric acid and acetonitrile as mobile phase. The method was validated for specificity, precision, linearity, accuracy, robustness and can be used in quality control during manufacture and for assessment of the stability samples of verapamil hydrochloride. To the best of our knowledge, a validated UPLC method which separates all the sixteen impurities disclosed in this investigation has not been published elsewhere. Total elution time was about 18min which allowed quantification of more than 100 samples per day. The analytical method discussed in British Pharmacopeia was pH sensitive and not compatible to LC-MS analysis but the method reported in this study is not involved any pH adjustment.
