(362e) A Metamodeling Approach to Support Design Optimization in the Sugarcane Industry.

Optimization of complex systems, such as the one in this paper, is restrictive since it involves the solution of a large set of equations that requires a large computational effort. A feasible alternative is to develop simplified models (metamodels) based on a set of simulations. These simplified models are constructed based on input-output relationships or simplification of the complex model as described hereafter. Once implemented, these metamodels are grouped to compose a superstructure representing the different alternatives to be optimized. Also, they are less computationally demanding.

Sugarcane simulation platform was used (CASTRO et al., 2018) to simulate four different process configurations: each sugarcane refinery has a particular set of machinery to produce sugar, ethanol, and power, which result in different process conversions and efficiencies. The simulations were carried out considering the following variables: three binary variables {Φ_j, θ_j, and Φ_S}, and three continuous variables {SSF, MIX, and BAGij}; the meaning of each variable is listed below. Simplified models (metamodels) were then obtained by correlating the results with the variables. The metamodel results are the products obtained from the sugarcane processing i.e. Bagasse, Sugar, first and second-generation Ethanol, and Power. These models will be used in a future study of the optimization of investment opportunities in the sugarcane industry.

• Φ_j to invest either in a high-efficient boiler and condensation-turbine to produce power or in a second-generation ethanol process.
• θ_jto invest or not in any option above.
• Φ_Sto invest or not in a bagasse cleaner station.
• SSF to bring straw from the field.
• MIX to produce more sugar or ethanol using the sugarcane juice.
• BAG_ij the surplus bagasse that can be exchanged between industries.

Method

The sugarcane juice can be used to produce sugar or ethanol. The decision variable to the industry to produce more sugar or more ethanol is represented by ‘MIX’: it is 0 for juice being used to produce only ethanol, 1 for only sugar, and it ranges between 0 and 1 for both being produced. The juice separation can be done after the milling process: which is the case of industry 3, or after the clarification process: this is the case of industry 2. Although most industries have some flexibility in their process, a total flexible refinery, i.e. industry able to produce only sugar and only ethanol is not common in Brazil. We compare the industrial mix for 4 different refineries in the next section.

This study comprises the following investment possibilities concerning the use of bagasse. First, to install a high-efficiency boiler and high-efficiency backpressure turbine, and a condensation turbine: this opportunity is represented by the binary variable Φ_j = 0. In this case, it has been considered that the pressure and temperature of the boiler steam are 65 bar(a) and 520 ºC. Second, to invest in a second-generation ethanol plant: this opportunity is represented by Φ_j = 1. Investing in neither option is represented by the binary variable θ where θ_j = 0 means that none investment is made in that industry ‘j’.

The fiber fraction in sugarcane and straw comprises the lignocellulosic materials, which are composed principally of cellulose, hemicellulose, and lignin. The second-generation process converts hexoses (glucan) from cellulose and pentoses (xylan) from hemicellulose to ethanol. Table 2 describes the composition of the fiber in bagasse in terms of the amount of glucan, xylan, and lignin on a dry basis. The straw was considered to have the same composition as in the bagasse. For the power generation process, the net-calorific-value or lower-heating-value, LHV, for bagasse and straw is obtained by equation 01 adapted from Hugot (1986)

Results

The industrial process was segregated into three parts. The first part is the juice extraction; in this part, sugarcane is divided into juice and bagasse. The second is the juice processing which consists of the production of first-generation ethanol or sugar. And the last part is the bagasse processing; in this part electricity or second-generation ethanol is produced using bagasse and straw as feedstock.

1. a) Juice extraction

This part aims to separate the fiber in a solid stream, from the sucrose in a liquid stream. The juice is the liquid stream, which is rich in sucrose, and it is used to produce sugar or first-generation ethanol, or both. The bagasse is the solid stream used to produce electricity or second-generation ethanol.

The main characteristics of each industry are shown in Table 3. Sugarcane preparation and extraction uses more than 25% of the total industrial power requirements (REIN, 2007). Shredder, knifing, and milling can be driven using steam or electricity. Industry 1 and 2 uses steam to drive knifing, shredder, and milling; industry 3 uses electricity to drive knifing, and shredder and steam to drive milling; industry 4 uses electricity to drive knifing, shredder, and milling. As a result, each refinery as a different amount of surplus power, surplus bagasse, or ethanol 2G which are shown in part ‘c’ of this section.

1. b) Sugar and first-generation ethanol

Table 4 shows the 'industrial MIX' range for each refinery. Industry 1 produces only sugar, Industries 2 and 3 produce sugar and ethanol, and Industry 4 produces only ethanol. Industry 1 has no distillery installed; consequently, the molasse, which is a by-product from the sugar process is one of their commercialized products, i.e. molasse is not used to make ethanol in its industrial plant. Industry 2 MIX ranges from 1 to 0.84, i.e. this industry has the machinery to use the whole juice extracted to produce sugar; however, even when the MIX is 1, the molasse is used as feedstock to produce ethanol. Or else, in Industry 2, 16% of the juice can be used, mixed with molasse, to produce ethanol. Industry 3 it can deviate from 23% to 31% of the juice to the ethanol process. And industry 4 produces only ethanol.

The ethanol, sugar, and molasse production and how they depend on the simulated variables is obtained from equations 02, 03 and 04. These equations were obtained using a linear and o polynomial regression of the simulated data.

1. c) Steam and electricity generation or second-generation ethanol

In this part of the paper, the alternatives for bagasse processing are assessed. Bringing straw from the field (SSF) increases the amount of bagasse. Thus, the surplus bagasse can be used to produce electricity or second-generation ethanol, or else, can be exchanged between refineries. Table 5 and Table 6 summarize the main characteristics of the boiler, and the turbine currently installed, and the investment opportunity respectively concerning condensation-turbine scenario (Φ=0). For the second-generation ethanol scenario (Φ=1), the surplus bagasse and straw are used as a lignocellulosic material to feed the second-generation ethanol process. Table 7 summarizes the main parameters used for the simulation of the second-generation process. Equations 05, 06 and 07 presents the production of surplus bagasse, power, and second-generation ethanol.

As an example, Figure 01, 02, 03 and 04 show the simulated result for surplus power. For some scenarios, the consumption of electricity in producing second-generation ethanol (Φ=1) exceeds the production of electricity. Equation 06 was obtained by the regression of these data.

Conclusion

We obtained some metamodels that describe a highly complex process. Due to their simplicity, these metamodels can be used in the design optimization process. These simplified equations allow us to guarantee that the obtained optimal point is a global minimum on the whole parameter domain. Moreover, less computational effort is needed to solve an optimization algorithm.

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