Power-intensive processes, such as cryogenic air separation in which the major portion of the production cost is spent on energy/electricity, need to adopt a smart operational approach to ensure maximum usage of resources while minimizing the power cost. In this paper a state task network-based model of an air separation plant is designed to represent real world production constraints. A discrete time model-based production scheduling has been proposed and validated on several scenarios that reflects representative real time constraints. The optimal schedule found for every scenario chosen has shown efficient exploitation of all energy contracts and judicious utilization of the liquid products. Due to its granular and rigorous modeling approach along with computational efficiency, the proposed model manifests huge potential toward its implementation in a real-world air separation plant.

Differential Evolution (DE), an exceptionally simple and robust evolutionary algorithm with Lagrangian like method, was used for solving optimal control and parameter selection problems of fed-batch fermentation involving general constraints on state variables. These infinite dimensional optimization problems were approximated into the finite dimensional optimization problems by control vector parameterization. Integration of the dynamic penalty functions was used to ensure the feasible solution of these dynamic optimization problems. State and end-point constraints were included in the formulation to reflect the operating objectives. The optimization strategy was able to accommodate these constraints in a relatively simple manner. The concept of non-uniform discretization in control vector parameterization was evaluated and shown to give superior results. Simple representative problems as well as a complex, multiple feed problem of simultaneous saccharification and fermentation (SSF) has been considered here to demonstrate the validity of the proposed methodology. The proposed methodology yielded an increase in productivity of approximately 20% in the case studies considered here.

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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.