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The manufacturing industry plays a big role for pollution. To reduce impact of envoirmental pollution we have to concentrate on manufacturing sector. For the better manufacturing process which including controlling environmental pollution. This is motive of green manufacturing. Environmental pollution is happens due to friction between material and tool as well as cutting fluid. Considering cutting fluid as prime parameter in manufacturing. Most of the industries are taking care regarding selection of cutting fluid which enhances the property of cutting and reduces the environmental pollution. Temperature is main parameter while cutting as increases in temperature it will effects on surface roughness and accuracy of output with addition of tool failure. To reduce the effect of tool failure there is need of cooling system is needed. By comparing mineral oil with vegetable oil with respect to pollution giving better result. Various vegetable oils are used coconut oil, canola oil, sesame oil and palm oil in machining. Wettability and thermal conductivity will improve by addition of nanomaterials like MoS2, ZrO2, Al2O3, ZnO, TiO2, TiC-CO, Y-Zr2O3 etc. in green cutting fluids. More research work is needed to analyze effects of nanomaterials concentration in vegetable oils for better green cutting fluid development

Many microbial processes exhibit sustained oscillations under practical conditions. Often these oscillations are corrupted by noise from the environment. It is then important to filter out the noise and recover ‘true’ oscillations for subsequent studies. Previous studies have used algorithmic or neural or hybrid filters in conjunction with mathematical equations for the kinetics. However, this approach is limited because in real situations it is difficult to measure and model some of the variables. Therefore, a hybrid neural simulator (HNS) has been developed here and tested with the continuous fermentation with Saccharomyces cerevisiae. The HNS combines a hybrid neural filter (HNF) for the noise, a hybrid description of the fermentation kinetics and macroscopic balance equations for the bioreactor. The HNS achieved 96% recovery of noise-free oscillations, compared to 91% with an HNF and lower efficiencies with pure neural and algorithmic filters. The commonly employed extended Kalman filter was ineffective as a stand-alone device but contributed to good filtering by the HNF and the HNS, thus indicating that a proper distribution of variables between the mathematical and neural components can significantly improve the performances of both.

Despite its many useful properties, microbial production of poly-beta-hydroxybutyrate (PHB) is not yet commercially competitive with synthetic polymers. One reason is inadequate optimization of the fermentation under industrial conditions. In this study, a physiologically reasonable and experimentally validated kinetic model for PHB synthesis by Ralstonia eutropha was incorporated into a dispersion model to simulate a large fed-batch bioreactor. Solutions of the model indicated that cell growth and PHB synthesis were maximum at Peclet numbers (Pe) between 20 and 30, representing limited finite dispersion. At these Peclet numbers, the optimum feed rates also showed lower consumptions of the substrates than at Pe=0. Since complete dispersion was also difficult to achieve in production-scale bioreactors, these results pointed to the possibility of exploiting controlled dispersion for productivity enhancement.

The extended Kalman filter (EKF) is commonly used to filter out the inflow of noise into biological reactors. Its usefulness for bioreactors with monotonic outputs is well established. More recently, the EKF has been shown to be able to rescue stable periodic oscillations that have been distorted by noise. This study extends the use of the EKF to microbial oscillations that become chaotic under the influence of noise. As measured by the Lyapunov exponents of the noise-free and noise-filtered concentration profiles of a continuous culture of Saccharomyces cerevisiae, the filter is effective in recovering noise-free sustained oscillations from noise-induced chaos, but is less satisfactory for a culture with both deterministic and stochastic chaos. Other kinds of filters, employing artificial intelligence, are recommended in this case.

Monocyclic enzyme cascades are important regulators of biochemical reactions in living cells. The reaction network may have either one steady state or many, depending on its structure. The occurrence of multiple steady states has important biological implications. A simple graph-theoretic method has been applied to five reaction mechanisms—which together cover many common monocyclic cascades—to determine which mechanisms generate just one steady state and which ones allow more than one state. It is shown that an unstable steady state in a multiplicity region can be usefully exploited and that in some cases transitions may occur between uniqueness and multiplicity regions. The possible effects of such transitions are discussed.

The classic approach to the description of the temperature-dependence of enzyme activity lumps both reversible and irreversible inactivations into one step. This generates an erroneous dependence of the enzymatic rate on temperature and the duration of assay. A two-step ‘equilibrium model’ has been proposed recently to correct these weaknesses. However, this model too has some drawbacks. At short assay times, there are two peaks on the rate–temperature–time surface. This difficulty with fast assays is avoided by slow assays; however, the latter carry a greater risk of irreversible denaturation. This problem disappears on increasing the order of the irreversible step from one to two, suggesting that a second-order equilibrium model may be appropriate for some thermal inactivations.