Modeling And Optimization Of Lipase-Catalyzed Synthesis Of Adipate Esters Using Response Surface Methodology And Artificial Neural Network

Optimized Novozym 435 (Candida antarctica lipase B immobilized on acrylic resin)- catalyzed esterification of adipic acid and various monohydric alcohols was successfully performed. Solvent-based synthesis of adipate esters was carried out in small scale reaction using 30 mL screw-capped vials. The synthetic reaction was optimized by Response Surface Methodology (RSM) based on central composite rotatable design (CCRD) to evaluate the interactive effects of reaction parameters including temperature, time, enzyme amount and alcohol/acid molar ratio. A high percentage yield (>96.0%) using optimum conditions was obtained using a minimum amount of enzyme, which matched well with the predicted values. Artificial Neural Network (ANN) approach was also employed for the estimation of esterification yield in enzymatic synthesis of adipate esters. Various feedforward neural networks were performed using different learning algorithms. The best algorithm was found to be Levenberg–Marquardt (LM) for a network composed of seven hidden nodes with hyperbolic tangent sigmoid transfer function. ANN showed better prediction ability compared to RSM. A high coefficient of determination (R2) (>0.9) and a low mean absolute error (MAE) and root mean squared error (RMSE) for training, validating and testing data implied the good generalization of the developed models for predicting the reaction yield. In order to develop an efficient enzyme catalyzed process, alcohol specificity of enzyme in terms of chain length and structure in the synthesis of adipate esters was determined. Methanol, n-butanol, octanol, dodecanol, octadecanol, isobutanol, sec-butanol and tertbutanol were the alcohols used for this study. The results demonstrated that alcohol chain length and structure were determining factors that affect the optimum condition of the reaction parameters for the enzymatic synthesis of adipate esters. Minimum reaction time for achieving maximum ester yield was obtained for isobutanol. The initial rates of synthesis of adipate esters for primary and secondary alcohols were nearly the same. Kinetic study of the lipase-catalyzed adipate ester synthesis in solvent-based system was carried out as a preliminary step for future industrial scale bioreactor design. The reaction was found to obey the ping-pong bi-bi mechanism with alcohol inhibition. The coefficient of determination (R2) and MAE values between the simulated and experimental initial rates were determined as 0.9904 and 2.4×10-4 which shows a good quality of fit between the simulated and experimental values.In order to make the synthesis process more environmentally friendly and improve the productivity of the reactor to the highest amount, the reaction was performed in a solvent-free system using 0.5-L batch and 4-L continuous stirred tank reactors. Due to low solubility of the substrate and high viscosity of the reaction mixture, a continuous stirred tank reactor was used for continuous production of the ester. A high percentage conversion was achieved (about 96%) using minimum amount of enzyme (2.5%w/w) indicating the high efficiency of solvent free-system for synthesis of adipate ester. Continuous production of adipate ester was successfully performed with an average yield of 92.7% and high operation stability of enzyme for 28 hours, which is indicative of performing a successful process for the ester synthesis

Effect of Alcohol Structure on the Optimum Condition for Novozym 435-Catalyzed Synthesis of Adipate Esters

Immobilized Candida antarctica lipase B, Novozym 435, was used as the biocatalyst in the esterification of adipic acid with four different isomers of butanol (n-butanol, sec-butanol, iso-butanol, and tert-butanol). Optimum conditions for the synthesis of adipate esters were obtained using response surface methodology approach with a four-factor-five-level central composite design concerning important reaction parameters which include time, temperature, substrate molar ratio, and amount of enzyme. Reactions under optimized conditions has yielded a high percentage of esterification (>96%) for n-butanol, iso-butanol, and sec-butanol, indicating that extent of esterification is independent of the alcohol structure for primary and secondary alcohols at the optimum conditions. Minimum reaction time (135 min) for achieving maximum ester yield was obtained for iso-butanol. The required time for attaining maximum yield and also the initial rates in the synthesis of di-n-butyl and di-sec-butyl adipate were nearly the same. Immobilized Candida antarctica lipase B was also capable of esterifying tert-butanol with a maximum yield of 39.1%. The enzyme is highly efficient biocatalyst for the synthesis of adipate esters by offering a simple production process and a high esterification yield

Modeling and optimization of lipase-catalyzed production of succinic acid ester using central composite design analysis.

Esterification of succinic acid with oleyl alcohol catalyzed by immobilized Candida antarctica lipase B (Novozym 435) was investigated in this study. Response surface methodology (RSM) based on a five-level, four-variable central composite design (CCD) was used to model and analyze the reaction. A total of 21 experiments representing different combinations of the four parameters including temperature (35–65°C), time (30–450 min), enzyme amount (20-400 mg), and alcohol:acid molar ratio (1:1-8:1) were generated. A partial cubic equation could accurately model the response surface with a R2 of 0.9853. The effect and interactions of the variables on the ester synthesis were also studied. Temperature was found to be the most significant parameter that influenced the succinate ester synthesis. At the optimal conditions of 41.1°C, 272.8 min, 20 mg enzyme amount and 7.8:1 alcohol:acid molar ratio, the esterification percentage was 85.0%. The model can present a rapid means for estimating the conversion yield of succinate ester within the selected ranges

Global incidence and mortality rate of covid-19; Special focus on Iran, Italy and China

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new coronavirus, was diagnosed in China in December 2019. Around the globe, a total of 71 429 were infected up to February 17, 2020, with 98.9 of cases in China. On March 11, 2020, the World Health Organization (WHO) characterized the COVID-19 as 'pandemic'. Rapid positive worldwide incidence was the motivation behind this study to investigate the incidence and mortality globally. Methods: We used the data published by the WHO until March 9, 2020. Non-parametric tests and change point analysis were used for inferences. Results: Change point analysis for Iran and China and the world excluding China for the first 20 days revealed around 78, 195 and 2 further new cases per day, respectively. Italy had a big jump in incidence on the 36th day. Similarly, a sharp rise of positive cases was reported for the world on the 35th day. China successfully controlled the ascending reports of incidence on the 23rd day. Mortality in China and the world were almost similar for the first 20 days. There was an ascending incidence trend with two change points in Italy (30th and 36th days) and one change point in Iran on the 17th day. Mortality in the world jumped remarkably after day 42 with an estimation of almost more than 25 deaths per day. Conclusion: The incidence of COVID-19 varied by regions; however, after March 11, it became 'pandemic'. It was observed that after about 6 days with an emergence of sharp increase in incidences, there would be a mutation in mortality rate. On the other hand, the importance of 'on-time' quarantine programs in controlling this virus was confirmed. © 2020 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

The Association Between Obstructive Sleep Apnea and Depression in Older Adults

Background: Depression is the most frequent psychiatric disorder among the elderly. Obstructive sleep apnea (OSA) is a chronic and prevalent disease that has an ambiguous role in triggering depression. Several researches with contradictory findings have been performed about the association between OSA and depression. Objectives: This study aimed to investigate the association between OSA and depression among elderly. Patients and Methods: A total of 350 home residing elderly took part in this case-control study. The participants were selected using clustering method. All cases were divided into two groups of depressed and non-depressed using the geriatric depression scale (GDS). Then they were matched in age, gender, education and body mass index (BMI). Berlin questionnaire (BQ) was used to diagnose OSA. Data analysis was performed using Mann-Whitney U test, t-test, Chi-square and Fisher’s exact tests and odds ratio. Results: Totally, 60.6 % of depressed group and 18.9 % of non-depressed group were in high risk for OSA. A significant association was found between OSA and depression (P < 0.001, OR = 6.61, CI 95 % = 4.1 - 10.7). In addition, a significant association was found between gender and OSA (P = 0.008). Conclusions: OSA was associated with depression among the elderly patients. Given the high prevalence of OSA in older adults, implementation of screening methods is necessary to identify people at high risk of OSA

Effect of alcohol chain length on the optimum conditions for lipase-catalysed synthesis of adipate esters

Immobilized Candida antarctica lipase B, Novozym® 435, was used in the esterification of adipic acid and alcohols with different chain lengths (C1–C18). Optimum conditions for the synthesis of adipate esters were obtained using response surface methodology (RSM) with respect to important reaction parameters including time, temperature, substrate molar ratio and amount of enzyme. Alcohol chain length specificity of the enzyme in the synthesis of adipate esters was also determined. Minimum reaction time (215 min) for achieving maximum ester yield was obtained for butyl alcohol. Methanol required an increased time (358 min) and enzyme amount (10.2%, w/w) for attaining maximum yield. The maximum required temperature and time of 65°C and 523 min, respectively, were obtained for the synthesis of dioctadecyl adipate. The results demonstrate that alcohol chain length is a determining parameter in optimization of the lipase-catalyzed synthesis of adipate esters. Reactions under optimized conditions yielded a high percentage of esterification (>97%). The optimum conditions can be used to scale up the process

Application of artificial neural network for yield prediction of lipase-catalyzed synthesis of dioctyl adipate

In this study, an artificial neural network (ANN) trained by backpropagation algorithm, Levenberg–Marquadart, was applied to predict the yield of enzymatic synthesis of dioctyl adipate. Immobilized Candida antarctica lipase B was used as a biocatalyst for the reaction. Temperature, time, amount of enzyme, and substrate molar ratio were the four input variables. After evaluating various ANN configurations, the best network was composed of seven hidden nodes using a hyperbolic tangent sigmoid transfer function. The correlation coefficient (R 2) and mean absolute error (MAE) values between the actual and predicted responses were determined as 0.9998 and 0.0966 for training set and 0.9241 and 1.9439 for validating dataset. A simulation test with a testing dataset showed that the MAE was low and R 2 was close to 1. These results imply the good generalization of the developed model and its capability to predict the reaction yield. Comparison of the performance of radial basis network with the developed models showed that radial basis function was more accurate but its performance was poor when tested with unseen data. In further part of the study, the feed forward back propagation model was used for prediction of the ester yield within the given range of the main parameters

A multivariate modeling for analysis of factors controlling the particle size and viscosity in palm kernel oil esters-based nanoemulsions

An artificial neural network (ANN) was used to develop predictive models for studying and identifying factors that influence particle size and viscosity of sodium diclofenac-loaded palm kernel oil esters-nanomeulsions. The effect of four independent variables namely water content, oil and surfactant (O/S) ratio, mixing rate and mixing time were considered as inputs of the network trained. The particle size and viscosity of samples in various compositions prepared under different rate and time of high shear emulsification, were measured as output. Data, split into training, testing and validating sets, were modeled by incremental back propagation (IBP) algorithm. The developed model represents high quality performance of the neural network and its capability in modeling and identifying the critical factors that control preparation of the nanoemulsions. Water content with 30.82% importance was found to be the main parameter controlling the particle size and viscosity in the system, followed by O/S ratio, mixing rate and mixing time, with 27.28, 22.06 and 19.84% importance, respectively. The model was then employed to investigate the effect of composition and processing factors on particle size and viscosity of the nanoemulsions