Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing

Time-of-Use (TOU) electricity pricing provides an opportunity for industrial users to cut electricity costs. Although many methods for Economic Load Dispatch (ELD) under TOU pricing in continuous industrial processing have been proposed, there are still difficulties in batch-type processing since power load units are not directly adjustable and nonlinearly depend on production planning and scheduling. In this paper, for hot rolling, a typical batch-type and energy intensive process in steel industry, a production scheduling optimization model for ELD is proposed under TOU pricing, in which the objective is to minimize electricity costs while considering penalties caused by jumps between adjacent slabs. A NSGA-II based multi-objective production scheduling algorithm is developed to obtain Pareto-optimal solutions, and then TOPSIS based multi-criteria decision-making is performed to recommend an optimal solution to facilitate filed operation. Experimental results and analyses show that the proposed method cuts electricity costs in production, especially in case of allowance for penalty score increase in a certain range. Further analyses show that the proposed method has effect on peak load regulation of power grid.Comment: 13 pages, 6 figures, 4 table

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids

This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information

Genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing

Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA

Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process

The increasing challenges to the grid stability posed by the penetration of renewable energy resources urge a more active role for demand response programs as viable alternatives to a further expansion of peak power generators. This work presents a methodology to exploit the demand flexibility of energy-intensive industries under Demand-Side Management programs in the energy and reserve markets. To this end, we propose a novel scheduling model for a multi-stage multi-line process, which incorporates both the critical manufacturing constraints and the technical requirements imposed by the market. Using mixed integer programming approach, two optimization problems are formulated to sequentially minimize the cost in a day-ahead energy market and maximize the reserve provision when participating in the ancillary market. The effectiveness of day-ahead scheduling model has been verified for the case of a real metal casting plant in the Nordic market, where a significant reduction of energy cost is obtained. Furthermore, the reserve provision is shown to be a potential tool for capitalizing on the reserve market as a secondary revenue stream

Material and energy flows of the iron and steel industry: status quo, challenges and perspectives

Integrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies

Energy- and labor-aware production scheduling for sustainable manufacturing : a case study on plastic bottle manufacturing

Among the potential roadmaps towards sustainable production, the emerging energy-cost-aware production scheduling philosophy is considered as one promising direction. Therein, sustainability objectives, e.g., minimization of energy consumption/cost of production processes and stabilization of the electricity grid, can be achieved by manufacturing enterprises in a low-cost manner. However, these sustainability goals should be integrated with conventional production constraints besides the due date, e.g., reasonable labor cost based on work shifts, no production at weekends, and changeovers for different product types. This paper formulates a mixed-integer linear programming model for energy-and labor-cost-aware production scheduling at the unit process level, considering all the aforementioned constraints. A state-based energy model is used to reveal the energy consumption behavior of a process over time. It thus enables fine-grained energy-aware production scheduling. A case study is conducted for a blow molding process in a Belgian plastic bottle manufacturer. The measured power data enables to build an empirical energy model. The production scheduling is performed under real-time electricity pricing data. As a result, production loads are automatically shifted to the optimal periods. The optimal idle mode is automatically selected between production loads (powering off, idle, etc.). A schedule of joint energy cost and labor cost minimization is demonstrated to reduce 12% and 5% of total cost, compared to schedules that minimize energy and labor cost, respectively. In conclusion, although the labor wage is usually higher during periods with lower electricity price, energy and labor costs can be jointly optimized as a single objective to help factories minimize the production expenditure. (C) 2017 The Authors. Published by Elsevier B.V

On Idle Energy Consumption Minimization in Production: Industrial Example and Mathematical Model

This paper, inspired by a real production process of steel hardening, investigates a scheduling problem to minimize the idle energy consumption of machines. The energy minimization is achieved by switching a machine to some power-saving mode when it is idle. For the steel hardening process, the mode of the machine (i.e., furnace) can be associated with its inner temperature. Contrary to the recent methods, which consider only a small number of machine modes, the temperature in the furnace can be changed continuously, and so an infinite number of the power-saving modes must be considered to achieve the highest possible savings. To model the machine modes efficiently, we use the concept of the energy function, which was originally introduced in the domain of embedded systems but has yet to take roots in the domain of production research. The energy function is illustrated with several application examples from the literature. Afterward, it is integrated into a mathematical model of a scheduling problem with parallel identical machines and jobs characterized by release times, deadlines, and processing times. Numerical experiments show that the proposed model outperforms a reference model adapted from the literature.Comment: Accepted to 9th International Conference on Operations Research and Enterprise Systems (ICORES 2020

Process simulation as a decision support tool for biopharmaceutical process development in a South African context

In 2010 the incidence of neo-natal Group B Streptococcus (GBS) disease in South Africa was 3 per 1000 live births, more than twice the global average of 1.21 per 1000 live births. A recent life cycle impact assessment showed that a new vaccine against GBS disease in South Africa could have a potential value of $2 million -$ 4 million /kg (R 25 million - R 50 million /kg), as an attractive investment opportunity if a novel process can be successfully synthesised and licensed commercially. In the current global market new biopharmaceutical products require innovative and expedited development pathways. To achieve this, low-cost analytical tools with short turnaround times are needed to assist with process development decision making. Process simulation is one such tool which has been shown to be useful for evaluating process development decisions without the typically expensive investment required for experimental development of a new process. Three technology platforms (stainless steel, single-use, and a hybrid of both) were identified for use in a novel process to manufacture a GBS serotype III polysaccharide-protein conjugate antigen, for formulation into a vaccine against GBS disease. The three technology choices were compared and evaluated for the novel process at two fermentation scales of 20 L and 200 L, with cost of goods (COG) used as a comparison of economic performance for the six different scenarios. It was hypothesised that single use technology would yield the lower COG at both scales compared to stainless steel. Based on a literature survey, single use technology should require lower capital costs for pilot scale processes and should also have lower operating costs due to single use equipment not requiring sterilisation in place (SIP) and cleaning in place (CIP). It was further hypothesised that hybrid technology would yield the lowest COG by combining the best properties of stainless steel and single use technologies. A 3 x 2 factorial experiment design was used to structure the simulation exercise with three technologies at each of the two scales. A GBS serotype III process model was synthesised from literature sources, with fermentation stoichiometry based on an empirical material balance and fermentation kinetics fitted to a two-parameter Monod kinetic model. Equipment, consumables, and raw materials specifications were made using literature and empirical models. A base case simulation model, built for 20 L scale using stainless steel technology, was developed into the five additional scenarios. The best performing scenario in terms COG was then selected for sensitivity analysis using three parameters: fermentation titer, solid-liquid separation efficiency, and electricity dependence on diesel generation. At 20 L scale there was little difference in COG between the three technology options, with COG range across the three platforms of $9.7 million -$ 9.8 million /kg. At 200 L scale the best performing technology was stainless steel with a COG of $3.7 million /kg, which was$ 600 000 /kg less than the COG for single use of $4.3 million/kg. The difference was due to a higher cost of consumables for single use technology, and negligible differences in capital costs for single use over stainless steel. The effect of SIP and CIP costs on operating cost for stainless steel technology was found to be small compared to the greater consumables cost for single use. The 200 L stainless steel process was found to be sensitive to fermentation titer, with an increase in titer to 600 mg/L resulting in the lowest COG of$ 2.2 million /kg. The process was found to be least sensitive to electricity dependence on diesel, with only a $ 60 000 /kg increase in COG when 75% of electricity was derived by diesel generator. The hypothesis was disproved, with single use technology having the higher COG at both 20 L and 200 L scales compared to stainless steel technology. Hybrid technology did not yield the lowest COG either, instead resulting in a COG somewhere between stainless steel and single use. Stainless steel technology outperformed single use and hybrid technologies in COG at both scales, contrary to both parts of the hypothesis. A process to make a GBS vaccine could be profitable at scales of 200 L and above using stainless steel technology. Process simulation modelling was effective for evaluating process technology options without performing costly physical experiments. The simulation exercise provided valuable information on the economic impact of process development decisions as well as context specific information for the South African context. This methodology is therefore recommended for commercial biopharmaceutical process development, particularly for evaluating techno-economic scenarios in different decision pathways during the development process