Rajib Mukherjee

Sustainability in the context of process engineering

Computational process design for sustainability using various available techniques is still limited to computer-aided design featuring process optimization of energy and material flow plus minimizing greenhouse gas emission and water conservation. Sustainable process demands more, such as minimizing the impacts from other harmful emissions, discharges, waste creation, economic, and societal impacts. We have proposed an overall sustainability footprint, which in theory represents impacts of a process on all three domains of sustainability. This perspective article provides a critical analysis of attaining sustainability by minimizing this sustainability footprint using impact data as indicators. We also propose the use of the integration of the sustainability footprint in the computer-aided process design itself, rather than checking the impacts after the data have been collected on actual process options designed ahead of the analyses.

Design of novel polymeric adsorbents for metal ion removal from water using computer-aided molecular design

Heavy metals in drinking water act as contaminants that can cause serious health problems. These metal ions in drinking water are generally removed using cation exchange resins that are used as adsorbents. Generally, chelating resins with limited adsorption capacity are commercially available. Manufacturing novel resin polymers with enhanced adsorption capacity of metal ion requires ample experimental efforts that are expensive as well as time consuming. To overcome these difficulties, application of computer-aided molecular design (CAMD) will be an efficient way to develop novel chelating resin polymers. In this paper, CAMD based on group contribution method (GCM) has been used to design novel resins with enhanced adsorption capability of removing heavy metal ions from water. A polymer consists of multiple monomer units that repeat in a polymer chain. Each repeat unit of the polymer can be subdivided into different structural and functional groups. The adsorption mechanism of heavy metals on resin depends on the difference between activities in adsorbents and the bulk fluid phase. The contribution of the functional groups in the adsorption process is found by estimating the activity coefficient of heavy metal in the solid phase and bulk phase using a modified version of the UNIFAC GCM. The interaction parameters of the functional groups are first determined and then they are used in a combinatorial optimization method for CAMD of novel resin polymers. In this work, designs of novel resin polymers for the removal of Cu ions from drinking water are used as a case study. The proposed new polymer resin has an order of magnitude higher adsorption capacity compared to conventional resin used for the same purpose.

A Multi-Resolution Multi-Scale Computational Approach for Characterization and Analysis of Nanostructured Surfaces

Abstract Structures and property of surfaces are very important in different chemical, physical and biological processes. Understanding the surface characteristics in the microscopic level is essential in order to relate the surface characteristics to the performance of the product. R elation of product performance with surface characteristics helps to improve the product performance through optimizing the manufacturing process. Spatial distribution of surface features which defines the surface characteristics can be captured by the multi-resolution capabilities of wavelet transforms (WT) that can provide not only frequency localization but also spatial localization of feature signatures. A multi-scale molecular simulation can help to investigate the physical and chemical mechanism in the surface. Together with the multi-resolution surface feature analysis, the multi-scale molecular simulation will give a better understanding of the surface phenomena and its relation with the performance matrices. In this paper we discuss the application of this approach for surface characterization of Rh(111) in the adsorption desorption of CO. The adsorption on the surface depends on it s crystal lattice structures and the presence of defects. In the atomic level a first principle density functional theory (DFT) calculation is used to find the adsorption energy. In the mesoscopic level a kinetic Monte Carlo (KMC) model of the surface is used to simulate the temperature programmed desorption (TPD) from the surface. The on-top adsorption energy increases with surface defects in the form of vacancies which shifts the peak maximum of TPD to a higher temperature. To characterize the surface, fractal dimension of the crystal surface is found using wavelet transformation. The fractal dimension of the surface increases with presence of vacancies .

Optimizing spatiotemporal sensors placement for nutrient monitoring: a stochastic optimization framework

Nutrient monitoring is very important for the area of food–energy–water nexus. The sensor network for nutrient monitoring requires dynamic sensing where the positions of the sensors change with time. In this work, we have proposed a methodology to optimize a dynamic sensor network which can address the spatiotemporal aspect of nutrient movement in a watershed. This is a first paper in the series where an algorithmic and methodological framework for spatiotemporal sensor placement problem is proposed. Dynamic sensing is widely used in wireless sensors, and the current approaches to solving this problem are data intensive. This is the first time we are introducing a stochastic optimization approach to dynamic sensing which is efficient. This framework is based on a novel stochastic optimization algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). A small case study of the dynamic sensor placement problem is presented to illustrate the approach. In the second paper of this series, we will present a detailed case study of nutrient monitoring in a watershed.

Selection of Sustainable Processes Using Sustainability Footprint Method: A Case Study of Methanol Production from Carbon Dioxide

Abstract Chemical products can be obtained by process pathways involving varying amounts and types of resources, utilities, and byproduct formation. When such competing process such as six processes for making methanol is considered in this study, it is necessary to identify the most sustainable option. Sustainability of a chemical process is evaluated with indicators. These indicators individually reflect the impacts of the process on areas of sustainability. To choose among several alternative processes an overall comparative analysis is essential. A mixed integer optimization problem can be solved to identify the most economic among competing processes. This method uses economic optimization and leaves aside environmental and societal impacts. The method presented here rationally aggregates sustainability indicators into a single index called sustainability footprint ( D e ). Results from sustainability footprint are compared with those from solving a mixed integer optimization problem.