Search

This paper addresses the optimization of contracted electricity demand (CD) for commercial and industrial entities, focusing on cost reduction within the Brazilian time-of-use electricity tariff scheme. Leveraging genetic algorithms (GAs), this study proposes a practical approach to determining the optimal CD profile, considering the complex dynamics of energy demand on a city-like load. The methodology is applied to a case study at the Federal University of Pará, Brazil, where energy efficiency and demand response initiatives as well as renewable energy projects are underway. The findings highlight the significance of tailored demand management strategies in achieving energy-related cost reduction for large-scale consumers, with implications for economic efficiency in energy consumption. [ABSTRACT FROM AUTHOR]

Severe droughts, water-use conflicts, and minimum outflow requirements have caused infeasibilities in modeling large-scale complex hydrothermal systems. This paper presents and discusses a new formulation of objective function and constraints to better represent multiple water uses and deal with infeasibilities in the HIDROTERM model, a nonlinear programming (NLP) model developed to optimize the operation of the Brazilian interconnected hydrothermal system. The system is one of the largest in the world. In the last 10 years, approximately 72% of electricity consumed in Brazil has been supplied by hydropower plants, but with increasing shares from thermal, wind, and, more recently, solar power generation. Because of hydrological changes in recent years and the additional operational constraints imposed by environmental agencies, the use of models for decision making, such as HIDROTERM, frequently encounters the problem of infeasibility. To avoid this problem, a new objective function is proposed in HIDROTERM that incorporates economic penalties when the preset requirements are not met. Based on economic considerations, some hard constraints are removed from the constraint set and added to the objective function as penalty terms. These constraints are tightly linked with multiple water uses, including environmental protection, flood control, consumptive uses, and navigation purposes. The proposed change in the objective function and constraints avoids the problem of infeasibilities and makes it possible to apply the model to extreme conditions. Additionally, this formulation helps identify and minimize the risk, duration, and intensity of unavoidable non-compliance occurrences related to multiple water uses. The presented new version of the HIDROTERM model can be used to study and support decision making in the management and operation of large-scale hydrothermal systems. These systems can be formed by a set of hydraulically connected individual hydropower plants and reservoirs, located within a set of electrically connected subsystems. Each subsystem contains demands and other power plants, including thermal, wind, nuclear, and solar plants, and exchanges. The model has been developed with continued improvements since 2008 and applied to large systems such as the Brazilian Interconnected Power System (BIPS) with over 150 reservoirs. A graphical user interface is provided. The objective function is to minimize the total costs of additional thermal dispatch and exchanges, as well as avoid or minimize deficits in energy supply in subsystems, minimum releases, consumptive uses, and minimum storages in reservoirs. The decision variables are the power and nonpower releases for each hydropower plant and the thermal dispatches and exchanges for each subsystem. Some typical applications include (1) monthly planning operation of the system, considering a planning horizon from 3 to 5 years; (2) diagnostic studies about reliability and resilience of existing systems, processing the same system configuration to a set of different hydrological or energy demand scenarios; (3) impact analysis of changes in operational rules and constraints related to multiple uses of water; and (4) planning and evaluation of expansion alternatives. The model can be applied to a complete hydrothermal system or subsets of it, including subsystems, basins, cascades, or a single hydropower plant. [ABSTRACT FROM AUTHOR]

Background: The soybean market is representative of the world. Brazil is the largest producer and exporter of this crop and has low production costs but high logistical costs, which are influenced mainly by transport costs. Added to these characteristics, the disputed grain supply, the possibility of crop failure, and the randomness of some parameters that influence the soybean supply chain make decisions even more challenging. Methods: To mathematically model this problem, we carried out an analysis of the scientific production related to grain supply chain and the models used to address the problem, as well as a document analysis and a case study. Results: This paper proposes a new two-stage stochastic linear programming model with fixed recourse for tactical planning in the soybean supply chain from the perspective of the shipper under take or pay contracts over a one-year time horizon. The first-stage variables are the grain purchasing decisions and the volumes of rail and road transportation hired in advance. The model addresses 243 scenarios derived from four uncertainty sources: the purchase and sale prices of raw agricultural products on the spot market, the probability of crop failure, and the external demand. Conclusions: The model is successfully applied to a soybean trade firm in Brazil with expected gain of US$4,299,720 when using the stochastic model instead of the deterministic model. The stochastic model protected the firm from take or pay fines and crop failures, contracting a smaller volume of rail transport than what the company does. [ABSTRACT FROM AUTHOR]

In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

Copyright of Exacta is the property of Exacta - Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper evaluates the impacts of multiple water uses on the operation of a system of existing reservoirs that originally were designed for hydropower generation. The research reported in this paper utilizes a nonlinear optimization model, developed to optimize hydropower production for a system of interconnected reservoirs. This model was modified to include water withdrawals for consumptive uses. The formulated multiobjective optimization problem is solved by employing the weighting method. The formulation considers water allocation policy for consumptive uses and individual hydropower plants. By parametrically varying the weighting coefficients, the tradeoff relationship (Pareto front) between hydropower generation and water demand for consumptive uses was traced out. The modified model is applied to the São Francisco River Basin in Brazil, where there are several hydropower plants in operation with a total installed capacity of 10.7 GW. The river basin is an important agricultural land that requires water supply for crop irrigation. The power generation loss as a result of increased water demand due to consumptive uses was evaluated. Water demand scenarios range from current estimated consumptive uses to the amount expected to be granted by the federal government in the future. It is concluded that these demands cannot fully be met in critical drought periods, and the proposed methodology can be helpful in leading to more equitable solutions among competing water users. [ABSTRACT FROM AUTHOR]

This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computa-tional phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (Î,) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India. [ABSTRACT FROM AUTHOR]

• A waste heat recovery for an offshore oil platform was analyzed. • The heat recovery device is the most irreversible in the organic Rankine cycle. • The organic Rankine cycle contributes to electricity generation up to 20.3%. • The organic Rankine cycle contributes to increase up to 11% in overall efficiency. • A net present value of up to USD 16.84 million is possible with the proposed change. The organic Rankine cycle has been widely explored for energy recovery from several heat sources. In this paper, the maximization of the energy recovery of the exhaust gases from the General Electric LM 2500 gas turbines in a floating production offloading unit is studied in order to meet the demands of heat (pressurized hot water) and electricity over oil production lifetime. An off-design model of the system is developed to check the viability of an organic Rankine cycle under dynamic operating conditions. Energy and exergy analysis of the proposed system are conducted to determine main irreversibilities and performance characteristics of the system and, finally, the economic benefits of the energy recovery system application are discussed. The organic Rankine cycle is very flexible in the heat recovery in dynamic demand conditions, contributing up to 20.3% in electricity generation, which causes an increase in overall system efficiency of up to 11.3% and up to 18.3% in the utilization factor, as well as an average reduction of 22.0% in carbon dioxide emissions in the floating production offloading unit electricity generation process. The exergy efficiency of the system reaches 55.8% and the heat recovery devices are the most irreversible equipment in organic Rankine cycle, contributing up to 68% of the total exergy destroyed. The economic analysis demonstrated the attractiveness of the organic Rankine cycle implementation, allowing a net present value to Brazil's current economic scenario of up to USD 16.8 million. [ABSTRACT FROM AUTHOR]

Leak noise correlators are commonly used to detect and locate leaks in buried water pipes. They use the cross-correlation function between leak noise signals measured using hydrophones or accelerometers placed on the pipe either side of the leak. The efficacy of a correlator is dependent upon knowledge of the speed at which the leak noise propagates along the pipe as well as how much it attenuates with distance. The leak noise is carried in a predominantly fluid-borne wave in the pipe, which is heavily influenced by the pipe and soil properties. Although the pipe properties can be determined relatively easily, estimation of the soil properties surrounding the pipe is more problematic. It is desirable to have an accurate estimate of the soil properties, so that current models can be developed and used to improve understanding of leak noise propagation and hence leak detection capabilities. In this paper a novel approach to determining the bulk and shear moduli of the soil from measurements of the predominantly fluid-borne wave in a buried plastic pipe, is described. The measured data are compared with corresponding data predicted from a model, and the soil properties are determined using an optimization algorithm. The method is applied to two different sites, one in the UK, where the soil properties surrounding the pipe are representative of sandy soil, and one in Brazil, where the surrounding soil has properties that are representative of clay soil. It is found that the bulk and shear modulus can be estimated in the pipe buried in sandy soil, but in the clay soil it is only possible to estimate the shear modulus. • A new approach to determine the bulk and shear moduli of the soil is described. • The approach is based on measurements of the predominantly fluid-borne wave (s=1) in a buried plastic pipe. • Two experimental test sites with different geometry and properties are used to demonstrate the efficacy of the method. • In sandy and clay soil, the soil shear modulus can affect the speed of s=1 wave in the pipe. • The attenuation of s=1 wave is affected by the soil bulk modulus in sandy soil, and shear modulus in clay soil. [ABSTRACT FROM AUTHOR]

Copyright of Exacta is the property of Exacta - Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The structural engineering community in Brazil faces new challenges with the recent occurrence of high intensity tornados. Satellite surveillance data shows that the area covering the south-east of Brazil, Uruguay and some of Argentina is one of the world most tornado-prone areas, second only to the infamous tornado alley in central United States. The design of structures subject to tornado winds is a typical example of decision making in the presence of uncertainty. Structural design involves finding a good balance between the competing goals of safety and economy. This paper presents a methodology to find the optimum balance between these goals in the presence of uncertainty. In this paper, reliability-based risk optimization is used to find the optimal safety coefficient that minimizes the total expected cost of a steel frame communications tower, subject to extreme storm and tornado wind loads. The technique is not new, but it is applied to a practical problem of increasing interest to Brazilian structural engineers. The problem is formulated in the partial safety factor format used in current design codes, with an additional partial factor introduced to serve as optimization variable. The expected cost of failure (or risk) is defined as the product of a limit state exceedance probability by a limit state exceedance cost. These costs include costs of repairing, rebuilding, and paying compensation for injury and loss of life. The total expected failure cost is the sum of individual expected costs over all failure modes. The steel frame communications tower subject of this study has become very common in Brazil due to increasing mobile phone coverage. The study shows that optimum reliability is strongly dependent on the cost (or consequences) of failure. Since failure consequences depend on actual tower location, it turns out that different optimum designs should be used in different locations. Failure consequences are also different for the different parties involved in the design, construction and operation of the tower. Hence, it is important that risk is well understood by the parties involved, so that proper contracts can be made. The investigation shows that when non-structural terms dominate design costs (e.g., in residential or office buildings) it is not too costly to over-design; this observation is in agreement with the observed practice for non-optimized structural systems. In this situation, it is much easier to loose money by under-design. When structural material cost is a significant part of design cost (e.g. concrete dam or bridge), one is likely to lose significant money by over-design. In this situation, a cost-risk-benefit optimization analysis is highly recommended. Finally, the study also shows that under time-varying loads like tornados, the optimum reliability is strongly dependent on the selected design life. [ABSTRACT FROM AUTHOR]

The Brazilian oil industry has gained new momentum after the discovery of large oil reserves in deep under-water. Numerous investments in the oil production chain are expected to support the scale of this operation. Given this context, the use of a decision support system (DSS), which encompasses the complexity of the oil supply chain to support investment decisions, becomes crucial. This paper proposes a DSS that is based on a mixed-integer linear programming (MILP) model that allows the evaluation of different investment alternatives in logistics networks, such as expanding the capacity for transport, handling, and/or storage. Additionally, the DSS is composed of a database structure, business intelligence environment, and graphical visualization tool. The features of the proposed system were evaluated in two case studies. The first case study assesses the synergies between two projects: specifically the expansion of the berthing capacity of vessels in a marine terminal, and the increase in the transport capacity of the pipeline that connects the marine terminal to a distribution base. The second case study evaluates the dependency of the investment in sections of a pipeline that connects a refinery to several distribution bases. These case studies demonstrate the potential use of a DSS that currently optimizes investments in the Brazilian petroleum supply chain. [ABSTRACT FROM AUTHOR]

The population increase and the growth of buying power of home appliances cause the need of electricity power to increase every year in Brazil. Electric dispatch is defined as the attribution of operational values to each generation unit inside a power plant, given some criteria to be fulfilled. In this context, an optimal dispatch schedule for hydroelectric units in energy plants provides a greater amount of energy to be generated with less consumption of water. This paper presents an optimization solution to solve this problem for an actual plant, using Genetic Algorithms. The underlying mathematical modeling is described in details and practical validation of the proposed approach is performed through simulation experiments. In the case study, results are analysed and compared to the actual system running in a real world plant. Finally, the generality of the proposed approach is discussed and possibilities of its use to solve the same problem to other hydroelectric plants are presented. [ABSTRACT FROM AUTHOR]

The sugar and alcohol sector has great relevance to Brazil's economy and, also, has been the center of new renewable energy possibilities. Besides ethanol itself, cogeneration is a recent advance which changed rules on production and sales of electricity in the country. The objective of this paper is to propose a master production planning model -- with mixed linear integer mathematical formulation -- considering products and byproducts of a alcohol and sugar factory. Not only production, sales and stocks decision are considered, but also alternatives of harvest methods with different efficiencies of straw, which can be used to cogenerate energy. Results were tested with data of a factory of western São Paulo, showing that it might be interesting to use less efficient methods of harvest to increase global results through byproducts. Preliminary results brought interest of refining data and parameters, having special attention to the decision support tool created. Future works include considering other process variables affected with straw portion on harvest. [ABSTRACT FROM AUTHOR]

Abstract: Many works have shown the potential of the Brazilian sugarcane industry as an electricity supplier. However, few studies have studied how this potential could be achieved without jeopardizing the production of sugar and ethanol. Also, the impact of modifications in the cogeneration plant on the costs of production of sugar and ethanol has not been evaluated. This paper presents an approach to the problem of exergy optimization of cogeneration systems in sugarcane mills. A general model to the sugar and ethanol production processes is developed based on data supplied by a real plant, and an exergy analysis is performed. A discussion is made about the variables that most affect the performance of the processes. Then, a procedure is presented to evaluate modifications in the cogeneration system and in the process, and their impact on the production costs of sugar, ethanol and electricity. Furthermore, a discussion on the renewability of processes is made based on an exergy index of renewability. As a general conclusion, besides adding a new revenue to the mill, the generation of excess electricity improves the exergo-environmental performance of the mill as a whole. [Copyright &y& Elsevier]

In the last 10 years much has been written about the drawbacks of radial projection. During this time, many authors proposed methods to explore, interactively or not, the efficient frontier via non-radial projections. This paper compares three families of data envelopment analysis (DEA) models: the traditional radial, the preference structure and the multi-objective models. We use the efficiency analysis of Rio de Janeiro Odontological Public Health System as a background for comparing the three methods through a real case with one integer and one exogenous variable. The objectives of the study case are (i) to compare the applicability of the three approaches for efficiency analysis with exogenous and integer variables, (ii) to present the main advantages and drawbacks for each approach, (iii) to prove the impossibility to project in some regions and its implications, (iv) to present the approximate CPU time for the models, when this time is not negligible. We find that the multi-objective approach, although mathematically equivalent to its preference structure peer, allows projections that are not present in the latter. Furthermore, we find that, for our case study, the traditional radial projection model provides useless targets, as expected. Furthermore, for some parts of the frontier, none of the models provide suitable targets. Other interesting result is that the CPU-time for the multi-objective formulation, although its endogenous high complexity, is acceptable for DEA applications, due to its compact nature. [ABSTRACT FROM AUTHOR]

The increasing rates of declines in anuran populations worldwide are creating demands for urgent strategies to maximize conservation efforts. This may be critical in regions for which few detailed data on diversity, abundance and distribution are available, such as in the Cerrado of Central Brazil. In this paper, we used a macroecological approach based on the extent of occurrence of 131 species of Anura (Amphibia) in the Cerrado region to design a regional network of potential areas that preserves all anuran species. The final network, obtained using a simulation annealing algorithm based on complementarity, has a total of 17 cells, widely distributed throughout the biome. Minimum costs solutions were obtained in respect to total human population size, soybean production and bovine density, because these are the factors associated with human occupation that historically are more likely to cause broad scale habitat losses. The macro-scale approach used here can provide overall guidelines for conservation and define the focus for more local and effective conservation efforts. [ABSTRACT FROM AUTHOR]

Abstract: Broad-scale correlations between species richness and human population suggest that processes driving species richness, mainly related to high ecological productivity, may also drive human populations. However, it is still under debate if this coincidence implies conflicts between biodiversity conservation and human development. In this paper, we analyzed the relationships among human population size, species richness and irreplaceability in Brazilian Cerrado. We analyzed a dataset with 131 species of anurans distributed in 181 cells with 1° of spatial resolution covering the biome. We found a positive correlation between human population size and anuran species richness (r =0.46; P =0.033 with 19.5 geographically effective degrees of freedom, v*), but the irreplaceability of each cell was poorly correlated with human population size (r =0.075; P =0.323; v*=173.9). The 17 cells in the 97 optimal reserve networks contained a total human population ranging from 2942,195 to 4319,845 people, representing on average 11.8% of the human population in the entire Cerrado grid. The comparison of these observed values with 10,000 values from randomly generated networks suggests a relatively high flexibility in optimal complementarity sets for reserve selection. Our results indicated that correlation between richness and human population does not necessarily result in conflicts, given the opportunities for conciliating conservation and development. However, the analyses performed here are initial explorations within the framework of conservation biogeography, so more detailed studies are necessary to establish conservation planning at regional and local scales. [Copyright &y& Elsevier]

Low salinity water injection (LSWI) has been studied by several researchers as an enhanced oil recovery (EOR) method in laboratory experiments, numerical simulations, and field applications. However, its effects on oil recovery remain uncertain due to the complexity of the interactions among crude oil, brine, and the rock surface, for both sandstone and carbonate reservoirs, particularly when they contain a diversity of clay minerals. This paper investigates LSWI performance and mechanisms in sandstone rock, brine, and light crude oil with low polar compounds from a reservoir in Brazil. Core flooding experiments and reservoir simulations were carried out to understand the influence of key parameters on oil recovery. Zeta potential measurements were performed to investigate the influence of the formation water and NaCl and CaCl 2 brine solutions on rock and oil surface charges. Increased oil recovery, Ca++ desorption from the rock surface, and a substantial pH increase in the effluent were noticed during LSWI. Based on the zeta potential measurement, a thicker water film was also formed on the rock and oil surface with brine dilution. A recovery factor of 64% was reached by testing LSWI from the beginning of the waterflooding (secondary mode) and this was the highest recovery of all the scenarios evaluated. Ten parameters were successfully history matched to core flood data, and optimization and sensitivity analysis showed that the concentration of Na+ and Ca++, and the water injection rate were the most important parameters in the LSWI process. Overall, this study sheds light on the LSWI mechanisms for the Brazilian oil field investigated and provides useful operational parameters for designing and achieving better LSWI performance. [Display omitted] • Core flooding performed with samples from an oil field in northeastern Brazil. • Higher oil recovery the earlier the diluted SFW cycling is applied. • Development of core flooding model with laboratory data for similar reservoirs. • Optimization and uncertainty assessment of critical operational parameters. [ABSTRACT FROM AUTHOR]

• General framework for the optimal net present value of smart appliance investment. • A Tree Algorithm with Interior Point speedup the framework in days. • Forty important economic cities in Brazil taken as case studies using real data. • Payback and return on investment measures are not enough for economic analysis. • Full set of results available online. Smart homes allow optimized energy usage, allowing households to reduce electricity bills or even make profits. By 2020, 20% of all households in Europe will be expected to become smart homes. Although smart homes seem to be the future for homes, many customers have the perception that a transition from current homes to smart ones is unprofitable. Adopting a smart home concept requires investments for which the households desire a positive return. A question in this context is the following: for a given household, when and/or what set of home appliances/technologies should be acquired so that the investment made by householder has a positive financial return? The available tool to answer that question can be time-consuming from a practical perspective. Based on our previous work, this paper proposes a framework to help the transition from current houses to smart homes considering customized electricity usage and economic measures. A tree algorithm is developed to decrease the time needed by an economic analysis of each possible acquisition combination of smart appliances or equipment for a given user. The proposed framework is tested on 40 cases covering all Brazilian capital cities, whose results are available online and may be used directly as an approximation for economic analyses. An example of one case is described in detail. Results show that the proposed tree algorithm is able to reduce days of CPU time to solve the problem and Net Present Value should be used as an economic measure to answer the aforementioned question. [ABSTRACT FROM AUTHOR]

After the 2010 to 2015 crisis in Brazil's sugarcane sector, mainly caused by government mandated fuel price ceilings, the sector has entered a new growth cycle spurred by implementation of the " RenovaBio " program in 2017. The government created RenovaBio to achieve two overarching goals: increase 2028 ethanol output by roughly 53% and incrementally increase this biofuel's share in the national energy matrix to meet GHG reduction targets established at the Paris Climate Conference - COP21. The study presented in this paper addresses the expansion of mill output to meet expected future demand by 2028 through application of a mixed-integer linear optimization model to (1) determine the amount of additional sugarcane production and mill processing capacity required to meet expected 2028 Brazilian ethanol and sugar demand and (2) optimize the location of this additional capacity to minimize production and transportation costs through the multimodal transportation network. Modeling results estimate that 151,106 t of additional sugarcane processing capacity is needed to meet forecast 2028 Brazilian demand for 41,106 m³ of sugarcane ethanol and 49,106 t of sugar. Expanded mill output will require that sugarcane cultivation increase over an additional 6% of the area defined by Brazilian Sugarcane Agroecological Zoning regulations as suitable for sugarcane cultivation. Following the model's recommended optimal spatial distribution of 2028 mill processing capacity, their output can be produced and transported at a total cost of US$ 458.65/m³ of ethanol and US$ 304.27/t of sugar, slightly below 2016 minimum total costs. • Optimal location of sugarcane crushing capacity in Brazil to meet estimated 2028 demand for ethanol and sugar. • MILP model applied to investigate the best locations for greenfield sugarcane mills. • Spatial optimization of sugar and ethanol supply and interregional freight flows through the multimodal network in Brazil. [ABSTRACT FROM AUTHOR]

In this paper the authors propose a global-local methodology for optimizing the short-term operation of hydroelectric plants. The authors determine the tradeoffs between minimizing the daily release from the plant and minimizing the number of startups and shutdowns of the generating units. The model is formulated as a mixed integer, nonlinear programming optimization problem with multiple objectives. The authors consider the nonlinearities of the generating units without simplifications or approximations. The authors develop a solution method that combines an evolutionary algorithm for the global search of the integer variables and a gradient-based local optimizer for the continuous variables. The local optimizer is embedded in the global search algorithm. Convergence is achieved by iterating between the global search and the local optimizer. The proposed methodology is applied to a moderately sized Brazilian hydroelectric plant that belongs to the national interconnected system. Additionally, a comparative study was conducted using historical operational records. The results demonstrate that the proposed methodology is feasible for online daily operations and delivers two specific benefits. The first is the efficiency gained, as the model seeks to operate the generating units as close as possible to their most efficient operating points. The second benefit is reduction of the units' maintenance costs, as the model minimizes switching on/off of the generating units. [ABSTRACT FROM AUTHOR]

Forest systems represent an important source of biomass for biorefineries. However, very complex dynamics impose difficulties in its efficient design. The present work proposes an optimization framework for biorefineries design that considers these dynamics. Four interconnected decision layers were considered: biomass supply, conversion processes, storage, and markets. The model is formulated as a MILP for maximizing the operational Net Present Value. An illustrative case-study was presented for lignin extraction in eucalyptus Kraft Pulp mills in Brazil. Land availability, forest productivity, and interest rates were fundamental for the location and sizing of production facilities. Logistics networks prioritized the minimization of sales taxes over transportation distance. The precipitation technology has become financially attractive in a scenario with a 50% lignin price increase. The optimal technology location was Sao Paulo due to lower opportunities costs and markets' vicinity. These outcomes highlight the importance of the whole supply chain evaluation in biorefinery design. Image, graphical abstract [ABSTRACT FROM AUTHOR]