Coal blending optimization under uncertainty

Shih, Jhih‐Shyang; Frey, H. Christopher

doi:10.1016/0377-2217(94)00243-6

Cited by 94 publications

(53 citation statements)

References 8 publications

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“…We use the chance-constrained (CC) method to model batches for finished alloy production. The CC method, first introduced by Charnes and Cooper [36], allows users to explicitly specify the confidence level of each batch to meet the specifications of final products [37][38][39]. This method provides an optimal batch plan based on the statistical parameters of the input materials.…”

Section: Chance-constrained Batch Planningmentioning

confidence: 99%

Data Mining Toward Increased Use of Aluminum Dross

Chang

Olivetti

Fjeldbo³

et al. 2015

J. Sustain. Metall.

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Recycling provides a key strategy to move toward a more sustainable society by partially mitigating the impact of fast-growing material consumption. One main barrier to increased recycling arises from the fact that in many real world contexts, the quality of secondary (or scrap) material is unknown and highly variable. Even if scrap material is of known quality, there may be finite space or limited operational flexibility to separate or sort these materials prior to use. These issues around identification and grouping given the operational constraints create limitations to simply developing an appropriate sorting strategy, let alone implementing one. This study suggests the use of data mining as a strategy to manage raw materials with uncertain quality using existing data from the recycling industry. A clustering analysis is used to recognize the pattern of raw materials across a broad compositional range in order to provide criteria for grouping (binning) raw materials. This strategy is applied to an industrial case of aluminum recycling to explore the benefits and limitations in terms of secondary material usage. In particular, the case investigated is around recycling industrial byproducts (termed dross for the case of the aluminum industry). The binning strategy obtained by the clustering analysis can significantly reduce material cost by increasing the compositional homogeneity and distinctiveness of uncertain raw materials. This result suggests the potential opportunity to increase low-quality secondary raw material usage before investment in expensive sorting technology.

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Section: Chance-constrained Batch Planningmentioning

confidence: 99%

Data Mining Toward Increased Use of Aluminum Dross

Chang

Olivetti

Fjeldbo³

et al. 2015

J. Sustain. Metall.

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“…Coal blending and distribution problem has drawn a lot of attentions, which can be found in (Sherali and Puri 1993;Shih and Frey 1995;Cao, Lin, and Yan 2006;Liu 2008;Yabin 2010;Yücekaya 2013, and references therein). Coal blending cost is minimised in Shih and Frey (1995) by incorporating the uncertainty of coal elements/attributes.…”

Section: Coal Logistics Optimizationmentioning

confidence: 99%

“…Coal blending cost is minimised in Shih and Frey (1995) by incorporating the uncertainty of coal elements/attributes. A total cost of the logistics system is taken into account in Cao, Lin, and Yan (2006), including railway transportation cost, procurement cost, ordering cost and holding cost.…”

Section: Coal Logistics Optimizationmentioning

confidence: 99%

Modelling of coal trade process for the logistics enterprise and its optimisation with stochastic predictive control

Cheng

Ning

Xia

et al. 2015

International Journal of Production Research

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In the paper, a typical coal trade process is described and modelled, where one logistics enterprise with blending equipments lies in the core and two types of common contracts are elucidated to define constraints. A mixed-integer model is built and featured by addressing contract violation, blending operation, real time price information and arbitrarily distributed stochastic demands. To deal with the stochastic demands, probabilistic constraints are formed. Accordingly, stochastic model predictive control (SM-PC) strategy with both receding horizon and decreasing horizon formulations is developed to handle the probabilistic constraints and exploit the value of newest price information. By solving a series of mixed-integer linear programs, optimal coal trade decisions for the logistics enterprise can be obtained, including procurement decision, selling decision and operational decision of the blending equipments. Thorough simulation experiments are carried out and compared under three different strategies, which interpret the effectiveness of the proposed strategy.

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“…CC optimization, first formulated by Charnes and Cooper [49], is one such method that has been implemented in various optimization problems governed by compositional uncertainty. These models have investigated metal production of both steel and aluminum, coal blending, and animal feed [45,[50][51][52][53][54]. Owing to its capability to explicitly consider, propagate and control uncertainty in a mix of uncertain constituents, CC optimization offers a great potential for an optimal multi-feedstock biodiesel problem.…”

Section: Chance-constrained (Cc) Optimization Model To Manage Uncertamentioning

confidence: 99%

Impact of feedstock diversification on the cost-effectiveness of biodiesel

et al. 2014

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While biodiesel production and consumption for use in transportation has risen considerably over the last decade, its competiveness in the marketplace is largely due to regulatory and fiscal support from governmental bodies, exceeding $25 billion in 2010 in the EU and US alone. The price of feedstocks represent 80-85% of the total biodiesel cost, and with over 350 different oil feedstocks available for blending, there is potential to optimize feedstock blends to reduce costs. This paper presents a chance-constrained optimization model that considers the technical constraints of conventional, first generation feedstocks, pricing trends, as well as the uncertainty and variation latent within these numbers. Further, the frequency with which a feedstock blend portfolio should be re-evaluated is considered through a case study. The model is then applied to a second case study for actual fuel constraint scenarios used in the EU and US. The results demonstrate the potential for substantial cost savings through targeted feedstock diversification, minimizing risks to producers from price fluctuations while still meeting technical fuel standards.

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Coal blending optimization under uncertainty

Cited by 94 publications

References 8 publications

Data Mining Toward Increased Use of Aluminum Dross

Data Mining Toward Increased Use of Aluminum Dross

Modelling of coal trade process for the logistics enterprise and its optimisation with stochastic predictive control

Impact of feedstock diversification on the cost-effectiveness of biodiesel

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