Enclave Optimization: A Novel Multiplant Production Scheduling Approach for Cryogenic Air Separation Plants

Pravin

Bhartiya

2021

Self Cite

Exploitation and implementation of renewable energy can substantially advance development, improve energy security, and alleviate the climate change problem. Integrating multiple renewable energy sources with grid power could potentially reduce the dependency on nonrenewable energy sources. However, because of the intermittent nature of the former, a tight integration between the supply and demand side management is necessary to facilitate uninterrupted power distribution and efficient energy consumption. This paper discusses an optimization framework that integrates supply and demand side management with application toward an energy-intensive cryogenic air separation process that is equipped with an in-house power generation system comprising multiple renewable and deterministic power sources as well as an energy storage system. The proposed framework integrates the power generation and purchase decisions along with the production complexities of the air separation plant (ASP). The optimal power generation schedule and the optimal production schedule provided by the proposed integration framework have been shown to minimize the total energy cost required to operate and meet the production requirement of the cryogenic ASP. An economic analysis is also performed to study the economic viability of including an in-house renewable source-based power generation and storage system with the ASP.

Section: System Descriptionmentioning

confidence: 99%

Integration of Supply and Demand Side Management Using Renewable Power Sources: Application on an Air Separation Plant

Pravin

Bhartiya

2021

Self Cite

“…The process network shown in Figure 2 constitutes a rigorous representation of the actual process, and minute nuances of this process are incorporated in the detailed scheduling frameworks presented by Misra et al (2017Misra et al ( , 2018). 9,14 The main bottleneck in extending the above-mentioned rigorous single plant/enclave formulations to simultaneously optimize productions in a large number plants leads to combinatorial explosion. With increasing number of constraints and variables, the branch and bound problem size increases exponentially making the problem computationally very expensive, if not intractable.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The effect of nonuniform discretization in reducing the computational effort was first introduced by Velez and Maravelias (2013). 19 Misra et al (2017Misra et al ( ,2018 applied the same nonuniform time discretization techniques for formulating single ASP 9 and enclave scheduling 14 frameworks to reap benefits from different demand windows of gaseous and liquid products. For the case study discussed in the following, we have considered the smallest time unit as 4 h. This could be expected to reduce the dimensionality of the variables as instead of 168(24 × 7) time slots, every variable will be calculated for 42(6 × 7) time slots over the same week.…”

Section: Enterprise-wide Production Andmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Planning Framework for Enterprise-wide Production and Distribution Network of a Cryogenic Air Separation Industry

Saxena¹,

Kapadi²,

et al. 2018

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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.

“…Misra et al proposed a multigrid discrete-time framework to solve the above-mentioned problem, in which calculations regarding the ship and jetty activities are performed at an hourly basis, whereas a daily discretized time scale is adopted to represent the constraints related to port inventories. The efficacy of adopting different discrete-time grids, based on the process requirements, toward increasing the computational efficiency of the model has been established by Velez and Maravelias and Misra et al , Using the multigrid discrete-time representation, Misra et al effectively capture the constraints and characteristics of the maritime inventory routing problem. However, that framework struggled to solve larger problem instances to the required optimality gap in a reasonable time.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Time-Based Framework for Maritime Inventory Routing Problem

Kapadi¹,

2020

Self Cite

The choice of a suitable time representation plays a major role in designing a computationally efficient planning/ scheduling framework. For the time representation to be effective, it needs to capture the nuances of the actual process, while ensuring that the resultant model is tractable and computationally efficient. In this paper, a novel time representation scheme, titled hybrid time discretization, which combines both continuous-and discrete-time representations in a single framework, is proposed and used to solve a complex multiproduct maritime inventory routing problem (MIRP) with undedicated compartments. Several continuous-and discrete-time-based frameworks have been proposed to solve the MIRP over the years. While single-/multigrid discretized frameworks do efficiently capture the time-sensitive nuances of the problem (such as active/inactive jetty hours and hourly/daily variations in the demands), they struggle to solve larger problem instances to the required optimality gap in a reasonable time. This is primarily due to the large disparities among the timings of different task components in the model. On the other hand, continuous-time representation can significantly reduce the problem size and computational effort; however, modeling the entire problem using the continuous-time-based framework could have problems in representing some time-sensitive intricacies in the demands and jetty/port activities. To allay the above-mentioned drawbacks, in this paper, a novel hybrid time representation is proposed, which combines a continuous-time representation with a multigrid discrete-time framework. The proposed novel framework is cast in a rolling horizon strategy and tested on various scenarios generated for the above-mentioned maritime transportation problem, which are shown to be significantly more complex than those presented in the literature. The proposed MILP framework is able to solve most of the test cases to the required optimality in a reasonable time without the need for heuristic or meta-heuristic assists. The proposed hybrid time representation could find significant utility in other lateral applications such as batch scheduling and inventory routing problems.