CN113739198A

CN113739198A - Method and device for optimizing operating parameters of biomass boiler

Info

Publication number: CN113739198A
Application number: CN202010468383.0A
Authority: CN
Inventors: 王磊; 亚恩·勒·毛莱克
Original assignee: Edf China Investment Co ltd
Current assignee: Edf China Investment Co ltd
Priority date: 2020-05-28
Filing date: 2020-05-28
Publication date: 2021-12-03
Also published as: EP3916301B1; EP3916301A3; EP3916301A2

Abstract

The present invention relates to a method for optimizing operating parameters of a biomass boiler, wherein the biomass boiler combusts a plurality of biomass fuels to release heat and produce ash, the method comprising: providing fuel information and a target boiler load for the biomass fuel; generating a series of design operating parameters for the biomass boiler; calculating unburned carbon content in the ash for design operating parameters based on fuel information for the biomass fuel and a target boiler load; and finding an operating parameter corresponding to a minimum value of the unburned carbon content as an optimized operating parameter in the series of design operating parameters. The invention can efficiently adjust the operation of the biomass boiler so as to adapt to the adjustment of the biomass fuel combination.

Description

Method and device for optimizing operating parameters of biomass boiler

Technical Field

The invention relates to the field of boiler control optimization of thermal power plants, in particular to control and optimization of operating parameters of a biomass boiler.

Background

In a thermal power plant, heat is released by a combustion reaction between combustible materials (such as coal of a coal-fired power plant and combustible biomass fuel of a biomass power plant) and oxygen in air, and then the heat energy is converted into electric energy by a power generation power device, and finally, the combusted flue gas is discharged through a chimney.

The core equipment of a thermal power plant is a steam generator or boiler that burns fuel by oxygen in the air to release heat. The heat then converts the water to steam for power generation or other industrial process heating. In biomass power plants, biomass such as wood, straw, bark and/or roots may be mixed (e.g., four substances) for use as a biomass fuel. However, the supply and price of biomass fuels often float and are seasonal compared to coal. For example, in china, it is common practice for biomass power plants to mix different biomass fuels and change the ratio of the components according to market price changes, so that they can be competitive in the market and can negotiate the price with the biomass fuel supplier at a better negotiation position.

Thus, the replacement of biomass fuel is critical to the ability of a biomass power plant to operate at low cost. However, due to seasonal and price fluctuations of biomass fuels, it is inevitable to replace the biomass fuels available in the season and at a reasonable price, sometimes even very frequently. Therefore, it is often necessary to ensure boiler performance and maintain smooth operation of the entire power plant at a significant time and economic cost.

In the prior art, it is common practice to empirically adjust the operating parameters of the boiler when replacing the biofuel, so as to optimize the combustion efficiency. Such adjustment operations are very complex, including adjustments such as air distribution and wind speed, grate vibration settings, etc., which are variable and interrelated. Such adjustments, therefore, require extensive and time-consuming testing by experienced operators or engineers with expert knowledge.

This operation, like all other operations that need to be performed empirically, is time consuming and lack of precision and may even lead to significant errors. Furthermore, such adjustments can easily take weeks, which can seriously impair production efficiency. Thus, current biomass power plants prefer to eliminate such adverse effects due to replacement and blending of new biomass fuels by purchasing expensive but familiar fuels at greater expense rather than further changing and blending the current season biomass fuels at the proper price in accordance with market changes. As a result, biomass power plants lose competitiveness in the electricity market.

Furthermore, to address the changing fuel markets, some power plants choose to store one or several biomass fuels in a warehouse so that they can continue to burn the same fuel in situations where some biomass fuels are temporarily unavailable.

However, this measure does not solve the problem, but may bring about new troubles. Since the biomass stored in the silo can cause a fermentation reaction with or without air. Then, depending on the storage conditions and duration, the fuel composition will change and cannot be considered to be the same fuel as before. Thus, biomass power plants that store biomass fuel are still faced with the same refueling situation. If adjustments to the boiler operating parameters are ignored, the boiler may be physically damaged due to poor combustion and result in economic losses.

It follows that while the development of biomass energy is supported by governments, the challenges of fuel replacement and blending limit its spread in more regions.

In the prior art, there have been some studies that have learned the behavior of boilers under different conditions by mathematically modeling them. However, these studies are currently only on a theoretical level of research and require the application of complex mathematical theories and the occupation of significant computational resources. Even though great efforts have been made in the prior art to simplify the models while retaining the ability to model accurately, these models are only simulation efforts and these studies are limited to single fuel combustion simulations, which is not consistent with the use of multiple biomass fuel blends in an actual production process, and thus none of these studies is practical for industrial production.

Therefore, in the prior art, when the biomass fuel mixture of the biomass power plant needs to be replaced, the operation of the boiler cannot be adjusted quickly and efficiently to optimize the combustion, which affects the economic benefit of the power plant.

Disclosure of Invention

It is an object of the present invention to overcome the disadvantages and problems of the prior art.

To this end, the invention proposes a method for optimizing operating parameters of a biomass boiler, wherein the biomass boiler burns a plurality of biomass fuels to release heat and produce ash, the method comprising:

-providing fuel information and target boiler load for the biomass fuel;

-generating a set of design operating parameters for the biomass boiler;

-calculating for the series of design operating parameters a corresponding unburned carbon content in the ash based on the fuel information of the biomass fuel and the target boiler load; and

-finding an operating parameter corresponding to the minimum value of the unburned carbon content among the series of design operating parameters as an optimized operating parameter.

With the present method, in the case of a boiler using a new biomass fuel mixture, the present invention can perform a series of calculations and then obtain optimized operating parameters, and then return recommended operating logic to the boiler operator, thereby helping to improve combustion efficiency and significantly shorten the tuning period. This is advantageous for the biomass power generation industry where frequent replacement of the fuel is required, especially in areas where fuel availability and market prices vary widely, to ensure efficiency and capacity of the biomass power plant.

Advantageously, according to an embodiment of the method of the invention, the unburned carbon content in the ash is calculated by means of a bed combustion model coupled to a suspension space combustion model.

Further, the bed combustion model is a zero-dimensional transient model, and the suspension space combustion model is a Continuous Stirred Tank Reactor (CSTR) suspension space combustion model.

Further, the bed combustion model uses:

-fuel information and target boiler load of the biomass fuel;

-the design operating parameters; and

-boundary conditions including radiant heat flux calculated by the suspension space combustion model.

Wherein the suspension combustion model uses a single core temperature to calculate radiant heat flux to calculate unburned carbon content in the ash by coupled solution of the bed combustion model and suspension combustion model.

Further, in the model, the following assumptions are made:

solid state reaction kinetics, including drying, pyrolysis, coke combustion and tar decomposition, modeled using the first order Arrhenius (Arrhenius) method:

Rate＝Aexp(-E/RT)

wherein A is the pre-coefficient, E is the activation energy, R is the gas constant, and T is the absolute temperature;

the gas phase is the ideal gas and the pressure is constant throughout the boiler;

the gas phase reaction is a fast reaction, in addition to the decomposition of the tar; and

for heat transfer between solid phases, there is no third order effect between the phases of the biomass fuel, and direct heat transfer between the solid phases is neglected.

Advantageously, the fuel information of the biomass fuel includes industrial and elemental analysis, porosity, mean particle size, density and composition, and the operating parameters include air flow, gas distribution, recycle gas flow and grate vibration specifications.

Advantageously, a numerical optimization method is used to generate a series of design operating parameters and find the optimized operating parameters.

Further, the numerical optimization method is an evolutionary algorithm, such as particle swarm optimization or a genetic algorithm.

Furthermore, the invention proposes a device comprising: a processor configured to execute instructions stored on a computer-readable medium to perform the above-described method.

Additional aspects, features and advantages of the present invention are discussed in the detailed description which follows, and will be readily apparent to those skilled in the art from that description or recognized by practicing the invention as described herein.

Drawings

It is to be understood that all features, variations and/or specific embodiments may be combined in various combinations, except where expressly contradictory or incompatible.

Other features and advantages of the invention will become apparent from a reading of the following non-limiting illustrative embodiments, taken in conjunction with the accompanying drawings, in which:

figure 1 is a workflow diagram according to an embodiment of the invention.

FIG. 2 is a schematic view of a bed combustion model coupled with a suspension space combustion model according to an embodiment of the invention.

Fig. 3 shows a heat transfer method according to an embodiment of the invention.

Detailed Description

The following are exemplary embodiments according to the present invention. The associated definitions below are used to describe exemplary embodiments and are not intended to limit the scope of the present invention. Since the embodiments described herein are exemplary, they can also be extended to modifications relating to the function, purpose, and/or structure of the present invention.

As explained previously, the calculation to optimize the operating parameters of a biomass boiler takes a significant amount of calculation time after the composition of the biomass fuel is adjusted. In response to this problem, the present invention modifies the model by making reasonable assumptions and focusing only on combustion efficiency, and the resulting reduced version of the model in conjunction with an optimizer can be used to calculate and optimize parameters for boilers combusting a variety of biomass fuels.

In the embodiment according to the present invention, the case where four different biomass fuels are mixed with each other is mainly discussed, which has not been discussed in the prior art, and the mixing of the four biomass fuels is very representative for some typical areas in China (e.g., Anhui, Henan and Hebei provinces). Of course, the invention is not only applicable to the four biomass fuels, but also can be optimized by the method of the invention for fewer or more cases.

As shown in fig. 1, the embodiment according to the present invention mainly comprises the following working logic:

I. providing fuel information and boiler load;

calculating the carbon content in the ash;

modifying boiler operating parameters with an optimizer such that carbon content in the ash is minimized;

and IV, returning the optimized boiler operation parameters.

These several links will be described in detail below.

I. Providing fuel information and boiler load

In this link, the operator may input the following biomass fuel information, for example, using a graphical user interface:

1. industrial analysis of each fuel;

2. elemental analysis of each fuel;

3. the bulk density and average particle size of each fuel;

4. composition of the mixed fuel;

5. average porosity of the mixed fuel.

Typically, the industrial and elemental analyses need not be updated each time a fuel change occurs, but rather the individual fuel components in the mixture need to be updated.

Calculating the carbon content in the ash

This link is the core of the present invention. In this implementation, the modeling of the biomass boiler combustion behavior is based on a method named "independent bed combustion model coupled with suspension space combustion model". As shown in fig. 2, the method generally employs a one-dimensional transient walking column model (independent bed combustion model M2 in fig. 2), and a three-dimensional Computational Fluid Dynamics (CFD) model (suspension space combustion model M1 in fig. 2). Existing studies have demonstrated good accuracy in various aspects of this approach, but it requires heavy computation, making it almost impossible to embed in online computation and/or performance optimization.

The inventors have found that the combustion efficiency of a boiler can be defined as the ratio of the amount of heat remaining in the ash after combustion to the total amount of heat available in the fuel, and that the measurement of the amount of heat requires specific laboratory equipment and is time consuming. For simplicity, the unburned carbon content of the ash can be used as a standard for boiler combustion efficiency. Thus, the present invention focuses on the unburned carbon content of the ash in order to observe the combustion efficiency. Based on this, the following simplification is made in the model according to the embodiment of the present invention to reduce the computational requirements and ensure the accuracy of the model.

1. A combustion factor. Only key combustion factors, such as maximum heat and mass transfer affecting parameters, are retained in the model. More precisely, the following assumptions have been made and verified.

a) All solid reaction kinetics (including drying, pyrolysis, coke burning and tar decomposition) were modeled using a first order Arrhenius (Arrhenius) method:

Rate＝Aexp(-E/RT)

where A refers to the pre-coefficient (which is constant for each reaction), E is the activation energy given by the reaction characteristics, R is the gas constant, and T is the absolute temperature of the reactants. For example, pyrolysis can be modeled as a single step model, where a ═ 1.5610 for a typical straw fuel¹⁰kg/s，E＝16600J/kg。

Due to the heterogeneous reaction nature of char combustion, the effect of particle size needs to be considered a characteristic area based factor. The particles can be described as uniform spherical particles of equal diameter (woody biomass) or hollow cylinders of equal inner and outer diameters (herbaceous biomass).

b) The gas phase is considered to be an ideal gas and the pressure is assumed to be constant throughout the boiler.

c) In addition to tar decomposition, gas phase reactions are considered rapid reactions. Thus, the gas combustion is instantaneous and does not take into account kinetic factors.

d) The heat transfer coefficient between the gas phase and the solid phase is according to the documents H.Khodaii, Y.M.Al-Abdeli and G.H.Y.Ferninando Guzzomi, "An overview of processes and considerations in the modeling of fixed-bed biomass combustion," Energy, vol.88, pp.946-972, 2015 "(" Process and considerations summary of fixed-bed biomass combustion modeling "," Energy ", first volume. 88, page 946-972, 2015. ) Is calculated from the available correlations. For example, the nusselt (nu) number may be calculated based on gas and solid properties in order to determine the Heat Transfer Coefficient (HTC) between the gas phase and the solid phase.

Nu＝2+1.1Pr1/3Re^0.6

Where Pr is the prandtl number of most gases around 0.71 under biomass combustion operating conditions. Re is the Reynolds number:

Pr＝0.71

where ρ gas density, u gas velocity, D characteristic diameter of the solid phase, μ dynamic viscosity. The Nusselt number can then be used to calculate the Heat Transfer Coefficient (HTC)

HTC＝λ/DNu

Where D is the characteristic diameter of the solid phase and λ is the gas phase thermal conductivity. λ can be used as a temperature dependent function in the literature. For example, the above document provides the following correlations

Wherein T is_gIs the gas phase temperature.

e) The heat transfer between the solid phases is calculated by the following method according to the invention.

Heat transfer between the mixed biomass fuel and the gas phase can be described as a complex multiphase heat balance problem. To the best of the inventors' knowledge, no previous studies based on biomass fuels have been performed due to the high complexity. In the present invention, a special method as shown in fig. 3 has been applied. Wherein the fuels 1, 2, 3, 4 each form a separate solid phase (rectangular box in fig. 3) while their gases stay in the 5 th phase (oval box in fig. 3). First, assume that there is no third order effect between phases (e.g., between fuel 1, fuel 2, and the gas phase). Second, direct heat transfer between the solid phases is neglected. The gas-solid heat transfer was calculated using the correlation mentioned in the above-mentioned document. Thus, in the present invention, each fuel (solid phase) can be freely exchanged with a separate gas phase, while heat exchange between fuels (solid phases) is neglected, so that the complex multi-phase heat balance problem is transformed into a series of two-phase heat transfer problems.

Accordingly, the following factors are ignored by the embodiments of the present invention because of their low degree of contribution:

second and higher order parameters of the Arrhenius process in solid state reactions.

Influence of particle shape

Intra-particle effect (taking into account uniform particle temperature)

Particle size distribution

Compressibility of gas

Pressure drop along the bed

Solid phase volume shrinkage

Gas phase combustion dynamics

Natural dependence of the specific heat of the Biomass (Biomass dependency of specific heat)

2. Bed combustion model basic elements. As shown in FIG. 2, in an embodiment of the invention, the bed combustion model M2 calculates the combustion efficiency (i.e., the unburned carbon content of the ash) based on the following three data sources.

User supplied data B0, i.e. target boiler load and current biomass fuel specifications, including industrial and elemental analysis, porosity, average particle size, density and their composition ratios.

Boiler operating parameter conditions B1, including air flow, air distribution, recirculated gas flow, and grate vibration specifications. The operating parameters are created by a random value generator, for example, in an embedded optimizer, which obeys constraints, as will be explained in detail below.

Boundary condition B2 returned by the suspension space combustion model M1. Guesses are used in the initialization step.

In the usual traveling column (one-dimensional transient) approach, the bed behavior in both directions is considered by assuming no change on the axis perpendicular to the direction of travel of the grate. Thus, the bed is discrete on two axes, the grate forward axis (further replaced by time discretization) and the fuel height axis, and introduces Partial Differential Equation (PDE) problems. The latter can be solved numerically using finite volume elements, but requires a large amount of computational resources and may cause frequent numerical problems in the calculations.

In the present invention, the spatial dispersion of the pattern in the fuel level on the grate is replaced by several combustion zones. Thus, the one-dimensional transient method is simplified to a zero-dimensional transient method. Mathematically, the original PDE problem was converted to an Ordinary Differential Equation (ODE) system, which could be solved in a number of more efficient and stable ways. Thus, the simplified bed combustion model is more stable and the calculation time is greatly reduced. By applying a radiant heat flux B2 as shown in FIG. 2 to the bed (either initially obtained by guesswork or calculated from a suspension space combustion model), the bed combustion model of the fuel can be solved and each gas component (e.g., NH)₃,CO₂、CO、H₂O、H₂、CH₄) As inlet boundary condition B3, back to the suspension space combustion model M1, as shown in fig. 2.

3. Basic elements of the suspension space combustion model. In an embodiment of the invention, as shown in FIG. 2, the suspension space combustion model M1 is coupled with the bed combustion model M2 under boundary conditions. By calculation of the bed combustion model, providing the inlet boundary condition of gas flux B3, the suspension space combustion model M1 will perform a calculation to return radiant heat flux B2 to the bed combustion model M2 as the third input to the bed combustion model.

A complete three-dimensional computational fluid dynamics (3D CFD) suspension combustion model can be used to understand the gas velocity field and detailed temperature distribution in the suspension section. However, the present invention is concerned only with the combustion efficiency of the boiler. Thus, the present invention does not employ a three-dimensional computational fluid dynamics approach in the suspension space section, but merely models the burnout zone as a Continuous Stirred Tank Reactor (CSTR). The core temperature was calculated in the burnout zone by providing the following conditions:

a) mass flux provided by the bed combustion model (inlet boundary condition B3 for the suspension space combustion model in fig. 2);

b) operating data for other air specifications, including secondary air and recycle gas;

c) heat loss from the entire boiler wall; and

d) heat of combustion in the gas phase.

Then, using the central temperature in the suspension space, the radiant heat flux of the bed (radiant heat flux B2 in fig. 2) can be calculated and sent as a boundary condition to the bed combustion model M2.

Through iterative calculation of the bed layer and the suspension part, a coupling solution is finally obtained, and further unburned carbon content in ash is obtained. Typically, the number of iterations required to complete such a calculation does not exceed 20. If convergence cannot be achieved, the initial guess should be modified rather than proceeding with more iterative calculations.

Modifying boiler operating parameters using an optimizer such that carbon content in ash is minimized

In this step, the previous model is embedded in the optimizer to find the best working conditions. The problem can be described using the following mathematical language:

-an objective function: minimizing unburned carbon content in the ash;

-design variables: primary and secondary air flow and distribution, recirculated gas flow, grate vibration specifications (spacing, duration and frequency);

-subject to limitations: boundary conditions for boiler operation such as minimum and maximum opening of the air valve, minimum and maximum vibration interval/duration/frequency, etc.

Due to the non-continuous nature of the problem, a non-gradient optimizer (numerical optimization method, including evolutionary algorithm) needs to be used to find the quasi-global optimization result. This optimizer numerical optimization method uses mechanisms inspired by biological or natural evolution. In an embodiment of the present invention, this is performed by the following two steps:

1. generating an initial set of individuals by a random value generator, comprising a series of initialized design operating parameters;

2. repeating a plurality of regeneration steps until predefined termination conditions (time limits, applicability, genetic algebra, etc.) are met, thereby obtaining optimized operating parameters including optimized air flow, air distribution, recirculated air flow, and grate vibration specifications, the steps specifically comprising:

a. evaluating the fitness of each individual in the current population;

b. selecting an individual most suitable for reproduction;

c. spawning the next generation by operations such as crossover and/or mutation; and

d. and replacing the least suitable individual in the current population with the newly created individual, and repeating the steps until the corresponding conditions are met to obtain the optimized operating parameters.

It is to be noted that, in the embodiment of the present invention, the genetic algorithm and the particle swarm optimization algorithm are employed, and their performances (convergence speed and calculation time) are different depending on different fuel data involved in the calculation.

Furthermore, in embodiments of the present invention, for a random value generator, a simple method similar to the Ziggurat algorithm can be embedded into the optimizer to create a series of constrained design variables for each computation step.

And IV, returning the optimized boiler operation parameters.

In an embodiment according to the present invention, in a fourth step after the third step of obtaining optimized operating parameters, the optimized boiler operating parameters may be returned to the operator for further use thereof via the graphical user interface. The operator will be able to use these results as guidelines for biomass refueling operations. Furthermore, according to the invention, the optimization parameters can also be transmitted to a Distributed Control System (DCS) of the power plant in order to automatically deliver the optimization recommended operating parameters to the control room. According to one embodiment, in a graphical user interface according to the invention, upon local request, one may choose to view more detailed information of the optimized boiler operation, for example graphically showing the fuel properties above the grate.

The whole process of implementing the four links can be controlled to be completed within 10 hours according to the coding environment and the skills. In addition, the invention can avoid using a computer graphic card for calculation. Since in biomass power plants there is little chance of refueling in less than 24 hours, the calculations according to the invention are suitable for on-line control and routine operation,

further, in combination with the above four links according to the embodiment of the present invention, a specific example according to the embodiment of the present invention is given below.

In this example, the design variables are limited to primary and secondary air flow and distribution. The circulating air flow and grate vibration specifications are fixed. In addition, the present example uses a 30MW boiler using a mixture of four biomass fuels, straw, wood, building board and bark, representing 2%, 68%, 12% and 18% by volume, respectively. Additional information for these four biomass fuels is shown in the table below.

In addition, the initial value of the primary air volume is 13kg/s by default, and the opening degree of the primary air valve A/B/C is set to 30/30/30, which means that the primary air is uniformly distributed in 3 areas below the grate.

After the above fuel information and boiler load information are provided, the unburned carbon ratio in ash can be calculated to be 29.1% by using the bed combustion model coupled to the suspension space combustion model in element II of the present invention.

The operating parameters are then optimized according to the present invention. To this end, the user may, for example, first enter fuel data and operating constraints in a graphical user interface. After verifying the requested boiler load, the user initiates the optimizer according to the present invention to perform the optimization calculations.

In this example, particle swarm optimization is employed. After calculation for about 30 minutes, the optimization criterion is met, and then the optimization result is output: the optimized allocation strategy is as follows: the primary air volume is 11.713kg/s, and the opening degree of the primary air valve A/B/C is 80/30.54/30.04 respectively. By such optimization, the unburned carbon ratio is reduced to less than 0.01%.

Furthermore, embodiments of the invention can take the form of a computer program product accessible by a computer and stored on a computer-readable medium providing program code for use by or in connection with a computer, processing device, or any instruction execution system. It should be noted that the computer or computer-readable medium described above can be any apparatus that can contain, store, communicate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be an electronic, magnetic, optical, or other semiconductor system (or apparatus or device).

In summary, the present invention provides an integrated solution for optimizing boiler operating logic for computing industrial scale applications. The user can provide a target boiler load and current fuel specifications including industry and elemental analysis, porosity, mean particle size, density and composition thereof, and then perform an optimization method according to the present invention, ultimately returning optimal boiler operating parameters including air flow, air distribution, recycle gas flow and grate vibration specifications, thereby enabling rapid and efficient adjustment of the combustion state of the boiler to different combinations of biomass fuels to fully utilize the different biomass fuels.

Various embodiments and variations and modifications will be apparent to those skilled in the art. It should be clear, in particular, that the features, variants and/or embodiments described in the present application can be combined with one another, unless clearly contradicted or incompatible. All such embodiments, variations and modifications are intended to be within the scope of the present invention.

Claims

1. A method for optimizing operating parameters of a biomass boiler, wherein the biomass boiler combusts a plurality of biomass fuels to release heat and produce ash, the method comprising:

-providing fuel information and target boiler load for the biomass fuel;

-generating a set of design operating parameters for the biomass boiler;

-calculating, for the series of design operating parameters, an unburned carbon content in the corresponding ash based on the fuel information for the biomass fuel and a target boiler load; and

-finding the operating parameter corresponding to the minimum value of the unburned carbon content in the series of design operating parameters as the optimized operating parameter.

2. The method of claim 1, wherein the unburned carbon content of the ash is calculated by a bed combustion model coupled to a suspension space combustion model.

3. The method of claim 2, wherein the bed combustion model is a zero-dimensional transient model and the suspension space combustion model is a Continuous Stirred Tank Reactor (CSTR) suspension space combustion model.

4. The method of claim 3, wherein the bed combustion model utilizes:

-fuel information and target boiler load of the biomass fuel;

-the design operating parameters; and

-boundary conditions including radiant heat flux calculated by the suspension space combustion model,

wherein the suspension combustion model calculates radiant heat flux using a single core temperature to calculate unburned carbon content in the ash by coupled solution of the bed combustion model and suspension combustion model.

5. Method according to claim 3 or 4, characterized in that the following assumptions are made in the model:

Rate＝Aexp(-E/RT)

wherein A is the pre-exponential coefficient, E is the activation energy, R is the gas constant, and T is the absolute temperature;

6. The method of claim 1, wherein the fuel information of the biomass fuel includes industrial and elemental analysis, porosity, mean particle size, density, and composition, and the operating parameters include air flow, gas distribution, recycle gas flow, and grate vibration specifications.

7. The method of claim 1, wherein a numerical optimization method is used to generate the series of design operating parameters and to find the optimized operating parameters.

8. The method of claim 7, wherein the numerical optimization method uses an evolutionary algorithm.

9. The method of claim 8, wherein the evolutionary algorithm is a particle swarm optimization or a genetic algorithm.

10. An apparatus, comprising: a processor configured to execute instructions stored on a computer-readable medium to perform the method of claim 1.