Analysis of Rainwater Harvesting Method for Supply of Potable Water: A Case Study of Gosaba, South 24 Pargana, India

In Gosaba, a village on the outskirts of South 24 Parganas, West Bengal, India, people experience a lot of problems related to shortage of potable water due to salinity and arsenic contamination in the supplied water. Rapid growth of industrialization, increased population, saline water intrusion etc. is causing a decrease in fresh water. Due to overuse of groundwater, GWT is declining rapidly in the Gosaba region. Moreover, seawater is intruding into the groundwater, causing pollution of surface water and a rise in Fe content, Cl content, arsenic content and salinity content in groundwater of that location. The runoff available from that amount of received precipitation is estimated using two empirical equations derived by Sir Aiexander Binnie; Ingels-De Souza and T.G. Barlow and the calculation confirms a good amount of runoff that can be utilized for harvesting in order to decrease the water scarcity of the location. The scarcity of fresh water in the Gosaba location can be minimized by adopting the rainwater harvesting (RWH) method, a sustainable process to obtain disinfected water at a very low cost. The technical part of the present study is to adopt RWH where rainwater is collected from rooftop of an institute building and to design tank where water can be stored and utilized further at minimum costs

Parametric Sensitivity of CSTBRs for Lactobacillus casei: Normalized Sensitivity Analysis

In this paper, a sensitivity analysis of a continuous stirred tank bioreactor (CSTBR) was conducted to determine a parametrically sensitive regime. The growth of a lactic acid bacterium, namely, Lactobacillus casei, in a pH-controlled CSTBR was considered as a process model. Normalized objective sensitivities of the minimum pH were determined with respect to input parameters. A generalized criterion for sensitivity was defined for determining the parametric range of three input variables, i.e., dilution rate base stream (θ), base concentration (R), and initial pH (pH0) for maintaining optimal pH range in the reactor. The system exhibits sensitive behavior for θ, R, and pH0, from 0.095 to 0.295, 0 to 0.865, and 4.42 to 4.77, respectively. The critical values of θ, R, and pH0 are 0.0195, 0.48, and 4.6, respectively. The mathematical model can also be used to determine a parametrically sensitive regime for other important parameters, namely, temperature, the concentration of metabolites, and other byproducts. The mathematical tool can also be used in bioreactor design and the improvement of control strategies

Parametric Sensitivity of CSTBRs for Lactobacillus casei: Normalized Sensitivity Analysis

In this paper, a sensitivity analysis of a continuous stirred tank bioreactor (CSTBR) was conducted to determine a parametrically sensitive regime. The growth of a lactic acid bacterium, namely, Lactobacillus casei, in a pH-controlled CSTBR was considered as a process model. Normalized objective sensitivities of the minimum pH were determined with respect to input parameters. A generalized criterion for sensitivity was defined for determining the parametric range of three input variables, i.e., dilution rate base stream (θ), base concentration (R), and initial pH (pH0) for maintaining optimal pH range in the reactor. The system exhibits sensitive behavior for θ, R, and pH0, from 0.095 to 0.295, 0 to 0.865, and 4.42 to 4.77, respectively. The critical values of θ, R, and pH0 are 0.0195, 0.48, and 4.6, respectively. The mathematical model can also be used to determine a parametrically sensitive regime for other important parameters, namely, temperature, the concentration of metabolites, and other byproducts. The mathematical tool can also be used in bioreactor design and the improvement of control strategies.</jats:p

Product Inhibition of Biological Hydrogen Production in Batch Reactors

In this paper, the inhibitory effects of added hydrogen in reactor headspace on fermentative hydrogen production from acidogenesis of glucose by a bacterium, Clostridium acetobutylicum, was investigated experimentally in a batch reactor. It was observed that hydrogen itself became an acute inhibitor of hydrogen production if it accumulated excessively in the reactor headspace. A mathematical model to simulate and predict biological hydrogen production process was developed. The Monod model, which is a simple growth model, was modified to take inhibition kinetics on microbial growth into account. The modified model was then used to investigate the effect of hydrogen concentration on microbial growth and production rate of hydrogen. The inhibition was moderate as hydrogen concentration increased from 10% to 30% (v/v). However, a strong inhibition in microbial growth and hydrogen production rate was observed as the addition of H2 increased from 30% to 40% (v/v). Practically, an extended lag in microbial growth and considerably low hydrogen production rate were detected when 50% (v/v) of the reactor headspace was filled with hydrogen. The maximum specific growth rate (µmax), substrate saturation constant (ks), a critical hydrogen concentration at which microbial growth ceased (H2*) and degree of inhibition were found to be 0.976 h−1, 0.63 ± 0.01 gL, 24.74 mM, and 0.4786, respectivel

Product Inhibition of Biological Hydrogen Production in Batch Reactors

In this paper, the inhibitory effects of added hydrogen in reactor headspace on fermentative hydrogen production from acidogenesis of glucose by a bacterium, Clostridium acetobutylicum, was investigated experimentally in a batch reactor. It was observed that hydrogen itself became an acute inhibitor of hydrogen production if it accumulated excessively in the reactor headspace. A mathematical model to simulate and predict biological hydrogen production process was developed. The Monod model, which is a simple growth model, was modified to take inhibition kinetics on microbial growth into account. The modified model was then used to investigate the effect of hydrogen concentration on microbial growth and production rate of hydrogen. The inhibition was moderate as hydrogen concentration increased from 10% to 30% (v/v). However, a strong inhibition in microbial growth and hydrogen production rate was observed as the addition of H2 increased from 30% to 40% (v/v). Practically, an extended lag in microbial growth and considerably low hydrogen production rate were detected when 50% (v/v) of the reactor headspace was filled with hydrogen. The maximum specific growth rate (µmax), substrate saturation constant (ks), a critical hydrogen concentration at which microbial growth ceased (H2*) and degree of inhibition were found to be 0.976 h−1, 0.63 ± 0.01 gL, 24.74 mM, and 0.4786, respectively.</jats:p