02362nas a2200277   4500008004100000022001400041245014500055210006900200260008000269300001200349490000700361520130400368653003001672653001801702653002201720653002301742653003301765653001601798100002101814700002801835700002101863700002201884700002101906700002601927856013101953       2009                        eng d  a1367-543500aMedia optimization for biosurfactant production by rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach0 aMedia optimization for biosurfactant production by rhodococcus e  aTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANYbSPRINGER HEIDELBERGcMAY  a747-7560 v363 a<p>This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques-(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)-were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI24)] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were similar to 3 and similar to 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were similar to 0.99 and similar to 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.</p>10aArtificial neural network10aBiosurfactant10aGenetic algorithm10aMedia optimization10aResponse surface methodology10aRhodococcus1 aPal, Moumita, P.1 aVaidya, Bhalchandra, K.1 aDesai, Kiran, M.1 aJoshi, Renuka, M.1 aNene, Sanjay, N.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/media-optimization-biosurfactant-production-rhodococcus-erythropolis-mtcc-2794-artificial-002129nas a2200157   4500008004100000245011200041210006900153260000800222300001600230490000700246520150800253100002701761700002701788700002601815856013001841       2016                        eng d00aMustard plant ash: a source of micronutrient and an adsorbent for removal of 2,4-dichlorophenoxyacetic acid0 aMustard plant ash a source of micronutrient and an adsorbent for  cOCT  a20087-200990 v233 aThe work highlights the utilization of an agricultural waste mustard plant ash (MPA) as a soil additive and an adsorbent. MPA was characterized by X-ray fluorescence (XRF), energy-dispersive X-ray spectroscopy (EDX), proximate analysis, CHNS analysis, Brunauer-Emmett-Teller (BET) surface area analysis, zeta potential measurements, Fourier transform infrared (FTIR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). XRF analysis confirmed the presence of CaO (31.35 %), K2O (18.55 %), and P2O5 (6.99 %), all of which act as micronutrients to plants. EDX also confirms high amount of elemental O, Ca, K, and P. The adsorptive ability of MPA was investigated using a commonly used herbicide, 2,4-dichlorophenoxyacetic acid (2,4-D), as a representative chemical. Batch adsorption experiments were conducted to study the effect of different operational parameters such as adsorbent dose, initial 2,4-D concentration, contact time, and temperature on the adsorption process. Data from experiments were fitted to various kinetic and isothermal models. The pseudo-second-order kinetic model was found to show the best fit (R (2) > 0.99), with the highest k (2) value of the order 10(5). Based on the study results, dosage of MPA/hectare for different crops has been recommended for effective removal of 2,4-D. To our knowledge, this is the first study in which MPA has been characterized in detail and investigated for dual applications (as an adsorbent and as a soil additive).1 aTrivedi, Nikhilesh, S.1 aMandavgane, Sachin, A.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/mustard-plant-ash-source-micronutrient-and-adsorbent-removal-24-dichlorophenoxyacetic-acid02198nas a2200217   4500008004100000022001400041245013700055210006900192260000800261520134700269653002301616653001001639653003601649653002701685653004001712100002601752700001801778700002701796700002601823856013101849       2019                        eng d  a0944-134400aMethodology of evaluating sustainability index of a biomass processing enterprise: a case study of native cow dung-urine biorefinery0 aMethodology of evaluating sustainability index of a biomass proc  cOCT3 a<p>Agriculture and its allied sector contribute significantly to the gross domestic product of every country. Several small-scale enterprises engaged in waste biomass processing have been setup recently. Such industrial setups not only help in solving the waste management issues but also play an important role in offering employment at the grass root level generating a significant social impact along with economic advantage to the local entrepreneur. Hence, assessment of such biomass processing enterprise (BPE) based on economic, environment, and social parameters has become necessary. In this paper, a general framework for sustainability assessment is discussed using a case study of cow dung-urine biorefinery as a representative BPE. Real-time data of BPE has been collected for evaluation and a sustainability index (SI) is evaluated using multicriteria decision method. The SI is calculated as per the weightage assigned and value function of the indicator and criteria. The SI for the BPE was observed to be 0.69 for the chosen set of criteria and indicator and weightages. A sensitivity analysis has been performed to check the dependence of the results on the weightages assigned to various criteria and indicators. It was also observed that the results were more sensitive to the indicators having a low value function.</p>
10aIndicator analysis10aMIVES10aMulticriteria decision analysis10aPerformance evaluation10aSustainability assessment framework1 aJoglekar, Saurabh, N.1 aDarwai, Vivek1 aMandavgane, Sachin, A.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/methodology-evaluating-sustainability-index-biomass-processing-enterprise-case-study-native