Municipal waste is one of the most hazardous components of developing countries. However, enzymes do provide an eco-friendly solution in this case. Amylase is an important enzyme in food, textile and pharmaceutical in...Municipal waste is one of the most hazardous components of developing countries. However, enzymes do provide an eco-friendly solution in this case. Amylase is an important enzyme in food, textile and pharmaceutical industry and can be used for bioconversion of waste. From the municipal solid waste we have isolated an amylase producing bacteria that can grow in the irritant municipal waste and help in their bio conversation. The bacteria were identified as Cronobacter sakazakii Jor52 (C2). The optimized media for maximum amylase production after 24 h of incubation, contains 2% starch, 0.6% peptone, 0.01% CaCl2, 0.05% KCl, 0.05% MgSO4 and 0.05% K2HPO4. The crude enzyme activity and stability study revealed that the amylase is stable within the pH 6 - 8 and temperature 30°C - 40°C and give maximum activity at 37°C at pH-8.展开更多
Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach.This has motivated the use of neural ne...Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach.This has motivated the use of neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system.However,due to the nonlinearities and multi-scale nature of combustion,the predicted solution often diverges from the true solution when these machine learning models are coupled with a computational fluid dynamics solver.This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation solvers.In the present work,a novel neural ordinary differential equations approach to modeling chemical kinetics,termed as ChemNODE,is developed.In this machine learning framework,the chemical source terms predicted by the neural networks are integrated during training,and by computing the required derivatives,the neural network weights are adjusted accordingly to minimize the difference between the predicted and ground-truth solution.A proof-of-concept study is performed with ChemNODE for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions.It is shown that ChemNODE accurately captures the chemical kinetic behavior and reproduces the results obtained using the detailed kinetic mechanism at a fraction of the computational cost.展开更多
文摘Municipal waste is one of the most hazardous components of developing countries. However, enzymes do provide an eco-friendly solution in this case. Amylase is an important enzyme in food, textile and pharmaceutical industry and can be used for bioconversion of waste. From the municipal solid waste we have isolated an amylase producing bacteria that can grow in the irritant municipal waste and help in their bio conversation. The bacteria were identified as Cronobacter sakazakii Jor52 (C2). The optimized media for maximum amylase production after 24 h of incubation, contains 2% starch, 0.6% peptone, 0.01% CaCl2, 0.05% KCl, 0.05% MgSO4 and 0.05% K2HPO4. The crude enzyme activity and stability study revealed that the amylase is stable within the pH 6 - 8 and temperature 30°C - 40°C and give maximum activity at 37°C at pH-8.
基金This work was supported by the U.S.Department of Energy,Office of Science under contract DE-AC02-06CH11357The research work was funded by Argonne’s Laboratory Directed Research and Development(LDRD)Innovate project#2020-0203.The authors acknowledge the computing resources available via Bebop,a high-performance computing cluster operated by the Laboratory Computing Resource Center(LCRC)at Argonne National Laboratory.
文摘Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach.This has motivated the use of neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system.However,due to the nonlinearities and multi-scale nature of combustion,the predicted solution often diverges from the true solution when these machine learning models are coupled with a computational fluid dynamics solver.This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation solvers.In the present work,a novel neural ordinary differential equations approach to modeling chemical kinetics,termed as ChemNODE,is developed.In this machine learning framework,the chemical source terms predicted by the neural networks are integrated during training,and by computing the required derivatives,the neural network weights are adjusted accordingly to minimize the difference between the predicted and ground-truth solution.A proof-of-concept study is performed with ChemNODE for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions.It is shown that ChemNODE accurately captures the chemical kinetic behavior and reproduces the results obtained using the detailed kinetic mechanism at a fraction of the computational cost.
