Optimization of Carbon and Nitrogen Sources for L-asparaginase Production by Enterobacter aerogenes using Response Surface Methodology

A full factorial central composite design (CCD) was applied to study various effects of sodium citrate, diammonium hydrogen phosphate (DAHP) and L-asparagine to determine the optimal concentration (γ) of these compounds on L-asparaginase production by Enterobacter aerogenes MTCC 2823 under shake flask fermentation conditions. A second order polynomial model describing the relationship between the variables and the L-asparaginase activity was fitted in coded units of variables. The statistical reliability and significance of the model was validated by F-test for analysis of variance at higher R2 value (R2 = 0.871). The optimum estimated concentration of sodium citrate (X1), DAHP (X2) and L-asparagine (X3) was 18.76, 5.72 and 8.58 g L–1 respectively with maximum L-asparaginase activity of 19.129 IU mL–1. The composite desirability of 98.38 % reveals the validity of the model and predicted values. The L-asparaginase activity was increased by 5.96 % than predicted activity, after optimization of carbon and nitrogen sources for L-asparaginase production by Enterobacter aerogenes MTCC 2823 using CCD

APPLICATION OF SENSITIVITY ANALYSIS FOR ASSESSMENT OF ENERGY AND ENVIRONMENTAL ALTERNATIVES IN THE MANUFACTURE BY USING ANALYTIC HIERARCHY PROCESS

Multi-criteria decision making (MCDM) was used to make comparative analysis of projects or heterogeneous measures for prioritization criteria and subcriteria simultaneously in a complex situation. The aim of this paper is to determine the alternatives and the sensitivity of main factors affecting water and energy consumption as well as environmental impact in a recycled paper manufacturing by using analytic hierarchy process (AHP). The AHP enables one to consider all the elements of decision process in a model, and to compare criteria and subcriteria of the model to find the best alternative. The AHP technique is applied through specific software package with user-friendly interfaces called Expert Choice. The results indicated that reduction of water consumption is the most important alternative for sustainable development in a recycled paper mill in Iran. Also, good housekeeping is the most sensitive criterion affecting the alternatives. The paper illustrates how the AHP method can help industrial management to overcome the energy usage and environmental impact in the manufacture

Optimal Management of a Hybrid Renewable Energy System Coupled with a Membrane Bioreactor Using Enviro-Economic and Power Pinch Analyses for Sustainable Climate Change Adaption

This study proposed an optimal hybrid renewable energy system (HRES) to sustainably meet the dynamic electricity demand of a membrane bioreactor. The model-based HRES consists of solar photovoltaic panels, wind turbines, and battery banks with grid connectivity. Three scenarios, 101 sub-scenarios, and three management cases were defined to optimally design the system using a novel dual-scale optimization approach. At the system scale, the power-pinch analysis was applied to minimize both the size of components and the outsourced needed electricity (NE) from Vietnam’s electrical grid. At a local-scale, economic and environmental models were integrated, and the system was graphically optimized using a novel objective function, combined enviro-economic costs (CEECs). The results showed that the optimal CEECs were $850,710/year,$1,030,628/year, and $1,693,476/year for the management cases under good, moderate, and unhealthy air qualities, respectively. The smallest CEEC was obtained when 47% of the demand load of the membrane bioreactor was met using the HRES and the rest was supplied by the grid, resulting in 6,800,769 kg/year of CO2 emissions

Rethinking of the future sustainable paradigm roadmap for plastic waste management: A multi-nation scale outlook compendium

The myriad consumption of plastic regularly, environmental impact and health disquietude of humans are at high risk. Along the line, international cooperation on a global scale is epitomized to mitigate the environmental threats from plastic usage, not limited to implementing international cooperation strategies and policies. Here, this study aims to provide explicit insight into possible cooperation strategies between countries on the post-treatment and management of plastic. First, a thorough cradle-to-grave assessment in terms of economic, environmental, and energy requirements is conducted on the entire life cycle across different types of plastic polymers in 6 main countries, namely the United States of America, China, Germany, Japan, South Korea, and Malaysia. Subsequently, P-graph is introduced to identify the integrative plastic waste treatment scheme that minimizes the economic, environmental, and energy criteria (1000 sets of solutions are found). Furthermore, TOPSIS analysis is also being adapted to search for a propitious solution with optimal balance between the dominant configuration of economic, environmental, and energy nexus. The most sustainable configuration (i.e., integrated downcycle and reuse routes in a closed loop system except in South Korea, which proposed another alternative to treat the plastic waste using landfill given the cheaper cost) is reported with 4.08 × 108 USD/yr, 1.76× 108 kg CO2/yr, and 2.73 × 109 MJ/yr respectively. To attain a high precision result, Monte-Carlo simulation is introduced (10,000 attempts) to search for possible uncertainties, and lastly, a potential global plastic waste management scheme is proposed via the PESTLE approach

Interpreting Patterns and Analysis of Acute Leukemia Gene Expression Data by Multivariate Statistical Analysis

DNA microarray technologies are leading to an explosion in available gene expression data which simultaneously monitor the expression pattern of thousands of genes. Gene expression data are characterized by a very high dimensionality (genes), a relatively small number of samples (observations), irrelevant features, and it leads to a collinearity and multivariate problem. In this paper, we propose a systematic approach to gene selection based on discriminant partial least squares (DPLS) and fuzzy clustering methods. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression

Wastewater Treatment System Optimization for Sustainable Operation of the SHARON–Anammox Process under Varying Carbon/Nitrogen Loadings

Partial nitritation (PN) coupled with the anaerobic ammonium oxidation (Anammox) process has improved ammonium removal in wastewater treatment plants (WWTPs). The operation conditions of this process, i.e., the dissolved oxygen (DO) and the influent ammonium and nitrite concentrations, drive the process to an equilibrium to suppress nitrite-oxidizing bacteria and achieve a proper nitrite over ammonium (NO2/NH4) ratio. This study aimed to implement a set of control strategies in a WWTP model BSM2-SHAMX, combining PN in a single reactor system for high-activity ammonia removal over nitrite (SHARON) to an Anammox reactor, using proportional–integrative–derivative (PID) control and model predictive control (MPC) in a cascade. For correct coupling, the PN should maintain an output NO2/NH4 ratio between 1 and 1.3, suitable for the Anammox process. In the cascade controller feedback loop, the primary control loop controls the NO2/NH4 ratio through the DO concentration from the secondary control loop, which guarantees better effluent nitrogen removal. The performance of the plant was assessed by evaluating the control strategies with different influent carbon/nitrogen (C/N) loadings. The study results showed that the MPC controllers provided better results, with an improvement of 36% in the operational cost compared to the base case with a cost around 26,000 EUR/d, and better nitrogen removal surpassing 90% removal, 10% more than the base case

ON-LINE NONLINEAR PROCESS MONITORING OF A PILOT-SCALE SEQUENCING BATCH REACTOR

Abstract. This article describes the application of on-line nonlinear monitoring of a sequencing batch reactor (SBR). Three-way batch data of SBR are unfolded batchwisely, and then a nonlinear multivariate monitoring method is used to capture the nonlinear characteristics of normal batches. The approach is successfully applied to an 80L SBR for biological wastewater treatment, where the SBR poses an interesting challenge in view of process monitoring since it is characterized by nonstationary, batchwise, multistage, and nonlinear dynamics. In on-line batch monitoring, the developed nonlinear process monitoring method can effectively capture the nonlinear relationship among process variables of a biological process in a SBR. The results of this pilot-scale SBR monitoring system using simple on-line measurements clearly demonstrated that the nonlinear monitoring technique showed lower false alarm rate and physically meaningful, that is, robust monitoring results

Multivariate Analysis and Monitoring of Sequencing Batch Reactor Using Multiway Independent Component Analysis

This contribution describes the monitoring on a pilot-scale sequencing batch reactor (SBR) using a batchwise multiway independent component analysis method (MICA) which can extract meaningful hidden information from non-Gaussian data. Given that independent component analysis (ICA) is superior to principal component analysis (PCA) to extract features from non-Gaussian data sets, the use of ICA may improve monitoring performance. The monitoring results of a pilot-scale SBR for biological wastewater treatment showed the power and advantages of MICA monitoring in comparison to conventional monitoring methods