Mathematical programming accelerates implementation of agro-industrial sugarcane complex

Colin, Emerson Carlos

doi:10.1016/j.ejor.2008.11.016

Cited by 25 publications

(26 citation statements)

References 8 publications

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“…This program, called National Alcohol Program (PROALCOOL) [5], allowed encouraging ethanol production and consumption [6]. The momentum for bioethanol in Argentina, from sugarcane was shaped legally with the sanction of Law 26.334 "Arrangements for the Promotion of Bioethanol Production" in 2008.…”

Section: Introductionmentioning

confidence: 99%

Isolation and characterization of Saccharomyces species for bioethanol production from sugarcane molasses: Studies of scale up in bioreactor

et al. 2016

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a b s t r a c tDecreases in oil reserves and gas fields around all over the world justify the deepening of studies to render viable the larger-scale use of new energy sources. Therefore, the use of microorganisms to convert sugars into ethanol is a feasible process to be performed in a short period of time and at low costs. In this context, this study aimed to select ethanol-producing yeasts, after isolating samples in molasses obtained from companies in the Province of Tucum an (Argentina) and grapes obtained from farms located in Cafayate (Salta, Argentina). Among the twenty-nine samples studied A2, A10 and A11 isolates showed higher ethanol productions of 12.87; 13.64 and 13.46% respectively. A2 showed a homogeneous growth meanwhile the growth of strains A10 and A11 was flocculent. Molecular taxonomic characterization of these isolates showed a percentage of similarity of 100% with the strain Saccharomyces cerevisiae. The behavior of the non-flocculent A2 strain at laboratory scale was faster using a sugarcane molasses based medium, reaching 11.36% ethanol without adding nutrients and other growth factors, probably because its disperse form facilitates the transfer of nutrients and products. These values were improved to 12.02% when the process was scaled up to a 10L bioreactor. All these studies allowed concluding that S. cerevisiae A2 strain is a promising microorganism for the production of bioethanol with potential environmental, energy and economic benefits to be projected into industrial scale.

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Section: Introductionmentioning

confidence: 99%

Isolation and characterization of Saccharomyces species for bioethanol production from sugarcane molasses: Studies of scale up in bioreactor

et al. 2016

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“…The sugarcane productivity ( cia P ), pol %cane ( ) and fibre ( ) vary with cut number (c), sugarcane variety (i) and period of time that it remains in field (a), then is advisable that harvesting in all plots is undertaken very close to the optimal time (Colin, 2009, Rudorff et al (2010). To force a plan with a cut of the sugarcane close to the optimal time the following produtivity function is proposed.…”

Section: Methodsmentioning

confidence: 99%

An optimization model for the combined planning and harvesting of sugarcane with maturity considerations

Ramos

Isler

Florentino

et al. 2016

Afr. J. Agric. Res.

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Planting and harvesting are important stages in the sugarcane crop cycle, because well planned planting and harvesting phases promote a series of benefits throughout the cultivation cycle and in the subsequent industrial use of the products. These benefits are operational, economic and environmental such as: improved utilization of the land area and transport logistics; an increase in sugarcane output; better cane reception in the factory; in administrative simplification of the industrial activities; in enhanced response to the demands of the industry; in cost planning; and in the control of pests and weeds. In this work a methodology of optimal cultivation planning to sugarcane planting and harvesting is proposed. The cultivation plan is for 5 years; and key decisions to be made in this period are to determine the planting date, the variety selection and the harvesting date corresponding for each plot such that the global production is optimized. We propose a mathematical model for this optimization task. The model uses computational and mathematical strategies to ensure that date of harvesting is always in period of the maximum maturation of the sugarcane and considers all demand and other operational constraints of the processing mill. The binary nonlinear optimization model was solved by a proposed genetic algorithm, giving an optimum plan with a potential sugarcane production 17.8% above production obtained by conventional means in the mill.

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“…Therefore, mathematical programming, particularly linear(LP) and mixedinteger linear programming(MILP), has been recognised as the most suitable strategy to handle and find solutions for such a complex problem. Different models have been suggested to optimise various parts such as transportations( [Ioannou, 2005, Kawamura et al, 2006, Milan et al, 2006, Dunnett et al, 2008, distributions ([Yoshizaki et al, 1996]), scheduling ([Grunow et al, 2007, Colin, 2009), and production planning( [Paiva and Morabito, 2009]) in the bioethanol/biofuel 6.1. Introduction supply chain.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimisation under deep uncertainty

Shavazipour

Stewart

2019

Oper Res Int J

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Most of the decisions in real-life problems need to be made in the absence of complete knowledge about the consequences of the decision. Furthermore, in some of these problems, the probability and/or the number of different outcomes are also unknown (named deep uncertainty). Therefore, all the probability-based approaches (such as stochastic programming) are unable to address these problems. On the other hand, involving various stakeholders with different (possibly conflicting) criteria in the problems brings additional complexity.The main aim and primary motivation for writing this thesis have been to deal with deep uncertainty in Multi-Criteria Decision-Making (MCDM) problems, especially with long-term decision-making processes such as strategic planning problems.To achieve these aims, we first introduced a two-stage scenario-based structure for dealing with deep uncertainty in Multi-Objective Optimisation (MOO)/MCDM problems. The proposed method extends the concept of two-stage stochastic programming with recourse to address the capability of dealing with deep uncertainty through the use of scenario planning rather than statistical expectation. In this research, scenarios are used as a dimension of preference (a component of what we term the meta-criteria) to avoid problems relating to the assessment and use of probabilities under deep uncertainty. Such scenario-based thinking involved a multi-objective representation of performance under different future conditions as an alternative to expectation, which fitted naturally into the broader multi-objective problem context. To aggregate these objectives of the problem, the Generalised Goal Programming (GGP) approach is used. Due to the capability of this approach to handle large numbers of objective functions/criteria, the GGP is significantly useful in the proposed framework. Identifying the goals for each criterion is the only action that the Decision Maker (DM) needs to take without needing to investigate the trade-offs between different criteria.Moreover, the proposed two-stage framework has been expanded to a three-stage structure and a moving horizon concept to handle the existing deep uncertainty in more complex problems, such as strategic planning. As strategic planning problems will deal with more than two stages and real processes are continuous, it follows that more scenarios will continuously be unfolded that may or may not be periodic. "Stages", in this study, are artificial constructs to structure thinking of an indefinite future. A suitable length of the planning window and stages in the proposed methodology are also investigated.Philosophically, the proposed two-stage structure always plans and looks one step ahead while the three-stage structure considers the conditions and consequences of two upcoming steps in advance, which fits well with our primary objective. Ignoring longterm consequences of decisions as well as likely conditions could not be a robust strategic approach. Therefore, generally, by utilising the three-stage structure, we may e...

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Mathematical programming accelerates implementation of agro-industrial sugarcane complex

Cited by 25 publications

References 8 publications

Isolation and characterization of Saccharomyces species for bioethanol production from sugarcane molasses: Studies of scale up in bioreactor

Isolation and characterization of Saccharomyces species for bioethanol production from sugarcane molasses: Studies of scale up in bioreactor

An optimization model for the combined planning and harvesting of sugarcane with maturity considerations

Multi-objective optimisation under deep uncertainty

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