The cryogenic air separation process is among the most energy-intensive operations and requires intelligent approaches to minimize its operational cost, the main constituent of which is the power cost. Some of the air separation plants operate in a co-operative manner with each other, and to capture the intricacies of these arrangements, a novel multisite framework is needed. In this paper, a novel approach called enclave optimization, which incorporates a small product exchange network among plants in the enclave, along with the multiplant production network, is introduced. The merits of enclave level framework lie in its ability to address the major challenges that originate from multiplant arrangements such as shared inventory, common customer and global liquid demands, etc. Motivated by the time scales of (i) gas and liquid demands and (ii) other operational factors, we adopt a nonuniform time discretization framework, which helps to define constraints regarding different products in various time scales, over the optimization horizon. The results show that the successful implementation of the nonuniform time discretization greatly reduces the overall number of constraints and variables involved in the optimization problem and makes the formulation computationally efficient. The above-mentioned nonuniform time, enclave level framework is applied to a real-world multiplant setting using representative scenarios provided by Praxair India Pvt., Ltd. The proposed model manifests its efficacy by optimizing those plants in a collaborative manner to determine an overall minimum production cost with high computational efficiency.

In this paper a supply chain optimization problem for packaged liquefied gaseous products is discussed from a warehouse stock management perspective. Liquefied gaseous products are transported across the supply chain using various reusable containers, and distribution to end users and collection after use of these containers are required to optimally operate an efficient and undisrupted supply chain. Along with traditional inventory routing, the framework presented in this paper explores some major aspects such as (1) resource estimation, (2) vehicle capacity utilization, and (3) inventory management. The proposed framework rigorously represents several nuances of the supply chain under different demand scenarios and generates an optimal delivery schedule, for the entire supply chain that minimizes the overall transportation cost by utilizing the available resources to its entirety. A rolling time horizon based planning strategy is also adopted to successfully implement the proposed framework for a larger operational horizons. Though the framework discussed here mainly represents packaged liquefied gaseous supply chain, the algorithm can be adopted and utilized for any supply chain with reusable/returnable transport packaging items.

In this paper, a novel mixed integer linear programming model is proposed for short-term planning of an enterprise wide production and distribution network for air separation industries. The objective is to develop a framework for the planning level, that is based on a more realistic abstraction of the scheduling level complexities, so as to evolve optimal implementable targets to the scheduling level. The approach is also oriented toward promoting a tighter coordination between the plants in an enterprise and to make intelligent use of the distribution network so as to minimize the overall production and distribution cost for the entire enterprise. The model involves intelligent formulation techniques to curtail the number of binary variables which in turn makes the model computationally efficient. A nonuniform time discretization framework is also adopted and the proposed framework is validated on a case study representative of typical industrial size production and supply chain network of air separation enterprises. Instead of rigorous modeling, the planning framework optimizes an industrial size test case to the required accuracy in stipulated time. This paper also outlines a decomposition based framework to achieve enterprise wide optimization in air separation industries.

Nanoparticles (NPs) having well-defined shape, size and clean surface serve as ideal model system to investigate surface/interfacial reactions. Ag and Al NPs are receiving great interest due to their wide applications in bio-medical field, aerospace and space technology as combustible additives in propellants and hydrogen generation. Hence, in this study, we have synthesized Ag and Al NPs using an innovative approach of ultra-sonic dissociation of thin films. Phase and particle size distributions of the Ag and Al NPs have been determined by X-ray diffraction (XRD) and transmission electron microscopy (TEM). Thin film dissociation/dissolution mechanism, hence conversion into NPs has been characterized by SEM-scanning electron microscope. EDXA & ICPMS have been performed for chemical analysis of NPs. Optical properties have been characterized by UV-Vis and PL spectroscopy. These NPs have also been investigated for their anti-bacterial activity against Escherichia coli bacteria. To the best of our knowledge, this is the first time when NPs has been synthesized by ultra-sonic dissociation of thin films. As an application, these NPs were used further for synthesis of nanocomposite polymer membranes, which show excellent activity against bio film formation.