Optimization of Inulinase Production from Garlic by Streptomyces sp. in Solid State Fermentation Using Statistical Designs

Plackett-Burman design was employed for screening 18 nutrient components for the production of inulinase using Garlic as substrate by Streptomyces sp. in solid-state fermentation (SSF). From the experiments, 4 nutrients, namely, NH4NO3, MnSO4·7H2O, Soya bean cake, and K2HPO4 were found to be most significant nutrient components. Hence, these 4 components are selected. The selected components were optimized using response surface methodology (RSM). The optimum conditions are NH4NO3—6.63 mg/gds, MnSO4·7H2O—26.16 mg/gds, Soya bean cake—60.6 mg/gds, and K2HPO4—5.24 mg/gds. Under these conditions, the production of inulinase was found to be 76 U/gds

Kinetic Modeling and Effect of Process Parameters on Selenium Removal Using Strong Acid Resin

Heavy metal pollution due to the contamination of Selenium above the tolerable limit in the natural environment is a challenging issue that environmental scientists face. This study is aimed at identifying ion exchange technology as a feasible solution to remove selenium ions using 001x7 resin. Parametric experiments were conducted to identify the optimal pH, sorbent dose and speed of agitation. Selenium removal efficiency of 85% was attained at pH 5.0 with 100 mg/L selenium concentration. The increase in resin dose was found to increase removal efficiency. However, metal uptake decreased. The experiments on the effect of concentration proved the negative effect of higher concentrations of selenium on removal efficiency. The ion exchange process was proved to be optimal at an agitation speed of 200 rpm and a temperature of 35 °C. Pseudo second order model was found to fit the kinetic data very well compared to the pseudo-first order model and the pseudo second order rate constant was estimated as 8.725x10-5 g mg-1 min-1 with a solution containing 100 mg/L selenium

Starch Wastewater Treatment in a Three Phase Fluidized Bed Bioreactor With Low Density Biomass Support

Aerobic digestion of starch industry wastewater was carried out in an inverse fluidized bed bioreactor using low density (870 kg/m3) polypropylene particles. Experiments were carried at different initial substrate concentration of 2250, 4475, 6730 and 8910mg COD/L and for various hydraulic retention time 40, 32, 24, 16 and 8h. Degradation of organic matter was studied at different organic loading rate by varying the hydraulic retention time and initial substrate concentration. From the results it was observed that the maximum COD removal of 95.6% occurs at an organic loading rate of 1.35 kg COD/m3/d and a minimum of 51.8% at an OLR of 26.73 kg COD/m3/d. The properties of biomass accumulation on the surface of particles were also studied. It was observed that a constant biomass loading was achieved over the entire period of operation

OPTIMIZATION OF INULINASE PRODUCTION USING COPRA WASTE BY Kluyveromyces marxianus var. marxianus

Kluyveromyces marxianus var. marxianus was found to secrete a large amount of extracellular inulinase in to the medium. The optimization of inulinase pro¬duction using copra waste as a carbon source was performed with statistical methodology based on experimental designs. The screening of eighteen nut¬rients for their influence on inulinase production was achieved using a Plackett––Burman design. Corn steep liquor, (NH4)2SO4, ZnSO47H2O, K2HPO4 and urea were selected based on their positive influence on inulinase production. The selected components were optimized using response surface methodology (RSM). The optimum conditions are: corn steep liquor – 0.0560 (g/gds), (NH4)2SO4 – 0.0084 (g/gds), ZnSO47H2O – 0.0254 (g/gds), K2HPO4 – 0.0037 (g/gds) and urea - 0.02147 (g/gds). These conditions were validated experimentally which revealed an enhanced inulinase yield of 372 U/gds

Artificial Neural Network Modeling of an Inverse Fluidized Bed Bioreactor

The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data obtained from an inverse fluidized bed reactor treating the starch industry wastewater. Experiments were carried out at various initial substrate concentrations of 2250, 4475, 6730 and 8910 mg COD/L and at different hydraulic retention times (40, 32, 24, 26 and 8h). It is found that neural network based model has been useful in predicting the system parameters with desired accuracy

Learning to View: Decision Transformers for Active Object Detection

Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment. In most robotic systems, perception is typically independent of motion planning. For example, traditional object detection is passive: it operates only on the images it receives. However, we have a chance to improve the results if we allow planning to consume detection signals and move the robot to collect views that maximize the quality of the results. In this paper, we use reinforcement learning (RL) methods to control the robot in order to obtain images that maximize the detection quality. Specifically, we propose using a Decision Transformer with online fine-tuning, which first optimizes the policy with a pre-collected expert dataset and then improves the learned policy by exploring better solutions in the environment. We evaluate the performance of proposed method on an interactive dataset collected from an indoor scenario simulator. Experimental results demonstrate that our method outperforms all baselines, including expert policy and pure offline RL methods. We also provide exhaustive analyses of the reward distribution and observation space.Comment: Accepted to ICRA 202

NANOCOMPOSITE APPLICATION FOR SELENIUM REMOVAL – PARAMETRIC STUDIES AND KINETIC MODELING

Nano composite material was synthesized using calcium hydroxy apatite and Phoenix Dactlyifera tree powder using wet chemical precipitation method and characterized using scanning electron microscopy and Fourier transform infrared spectroscopy. The influence of operating parameters namely initial pH (3 -11), selenium concentration (50 -200 mg L-1 ), nanocomposite dose (0.5 - 6.0 g L-1 ), presence of competitor chloride ion (0 -10 g L-1 ) and agitation speed (0 – 600 rpm) on the metal uptake was studied. A correlation relating nano composite dose and selenium uptake was proposed as selenium uptake = 202.3 (e-0.259* nanocomposite dose) he maximum uptake capacity of the nanocomposite was found to be 57.27 mg g-1 under optimal environmental conditions with an initial selenium concentration of 100 mg L-1 . Monolayer sorption mechanism, proposed by Langmuir isotherm, was found to apply for this process and the isotherm constants were determined. Modified Ritchie second order and pseudo second order models were fitted to the experimental data and pseudo second order model correlated well with rate constant of 1.5 x 10-3 g mg-1 min-1 and maximum uptake capacity of 70.92 mg g-1 at 32 °C with 100 mg L-1 initial metal concentration. Ritchie model rate constant was evaluated as 1.41×10-2 min-1 under similar process conditions

Starch Wastewater Treatment in a Three Phase Fluidized Bed Bioreactor With Low Density Biomass Support

Aerobic digestion of starch industry wastewater was carried out in an inverse fluidized bed bioreactor using low density (870 kg/m3) polypropylene particles. Experiments were carried at different initial substrate concentration of 2250, 4475, 6730 and 8910mg COD/L and for various hydraulic retention time 40, 32, 24, 16 and 8h. Degradation of organic matter was studied at different organic loading rate by varying the hydraulic retention time and initial substrate concentration. From the results it was observed that the maximum COD removal of 95.6% occurs at an organic loading rate of 1.35 kg COD/m3/d and a minimum of 51.8% at an OLR of 26.73 kg COD/m3/d. The properties of biomass accumulation on the surface of particles were also studied. It was observed that a constant biomass loading was achieved over the entire period of operation