Marine waste gas denitrification optimization control method based on big data

Abstract

The invention relates to the technical field of ship exhaust gas denitrification, in particular to a ship exhaust gas denitrification optimization control method based on big data, which comprises the following steps: collecting control parameters of an exhaust gas denitrification system, and regarding a set of the control parameters as population individuals; initial parameters of a genetic algorithm are preset, the initial parameters comprise the number of sets, the crossover probability, the mutation probability and the maximum number of iterations, and an initial population is output; carrying out iterative evolution on the initial population until the maximum number of iterations is reached, and outputting a population individual with the highest fitness as an optimal population individual; and taking the control parameters in the optimal population individuals as control parameter adjustment values of the waste gas denitrification system. In a complex and changeable environment, control parameters can be globally optimized, and falling into a local optimal solution is avoided, so that the overall performance of a denitrification system is improved.

Description

Background Art

[0002] As global environmental protection requirements become increasingly stringent, ship exhaust gas treatment has gradually become an important issue in the international shipping industry. Nitrogen oxide (NOx) emissions from ship exhaust are one of the main sources of pollution. Traditional denitrification methods, such as selective catalytic reduction (SCR) and selective non-catalytic reduction (SNCR), face problems such as space, cost and efficiency when used on ships.

[0003] Nitrogen oxide (NOx) emissions from ship exhaust are an important environmental issue. To reduce NOx emissions, we use exhaust gas denitrification technology (such as SCR). In order to achieve the best denitrification effect in actual operation, considering the multiple variables and dynamic changes involved in the operation of the ship, traditional optimization methods may find it difficult to find the global optimal solution. Summary of the invention

[0004] The main purpose of the present invention is to provide a ship exhaust gas denitrification optimization control method based on big data, which effectively solves the above-mentioned problems mentioned in the background technology.

[0005] The technical solution of the present invention is as follows:

[0006] First, a ship exhaust gas denitrification optimization control method based on big data is proposed, which includes the following steps:

[0007] S1. Collect the control parameters of the exhaust gas denitrification system and regard the set of control parameters as a population individual;

[0008] S2. Preset initial parameters of the genetic algorithm, which include the number of sets, crossover probability, mutation probability and maximum number of iterations, and output the initial population;

[0009] S3, iteratively evolve the initial population until the maximum number of iterations is reached, and output the population individual with the highest fitness as the optimal population individual;

[0010] S4. Using the control parameters in the optimal population individuals as control parameter adjustment values ​​of the exhaust gas denitrification system.

[0011] A further improvement of the present invention is that the control parameters of the exhaust gas denitrification system in S1 specifically include: ammonia injection amount, reaction temperature, catalyst activity, ambient humidity, and ship speed.

[0012] A further improvement of the present invention is that the specific content of S2 is: extracting the control parameter set of the exhaust gas denitrification system collected in S1 as a population individual; presetting the initial parameters of the genetic algorithm, the initial parameters including the number of sets, crossover probability, mutation probability and maximum number of iterations, and outputting the initial population {X1, X2, ..., X n}, where Xi is the i-th population individual in the initial population.

[0013] A further improvement of the present invention is that S3 comprises the following specific steps:

[0014] S31, iteratively evolve the initial population, and obtain the fitness value f(X i ), f(X i )=α×N(X i )+β×η(X i ), where N(X i ) is the control parameter set X i Nitrogen-containing gas emissions under i ) is the control parameter set X i The exhaust gas denitrification efficiency under the above conditions, α and β are important coefficients;

[0015] S32, select two population individuals to perform genetic crossover operation to generate new individuals. The formula for generating new individuals is: X ij =αX i +(1-α)X j ;X ij X is a population individual i With X j The size of α is 0-1 for the generated new individuals; the fitness value of the iteratively generated new individuals is calculated, and the fitness values ​​of the a-1 generation population individuals and the a generation population individuals are arranged in descending order, and then the n population individuals with the highest fitness values ​​are extracted as the new population, and the population individual with the highest fitness in the new population after a single iteration is recorded as the optimal population individual, and the fitness value f of the optimal population individual is recorded a , where a is the number of iterations, f a is the fitness value of the optimal population individual after the ath iteration;

[0016] S33, repeat the above steps until the number of iterations reaches the preset maximum number of iterations.

[0017] A further improvement of the present invention is that S4 comprises the following specific steps:

[0018] S41, preset fitness threshold b, extract the fitness value f of the optimal population individual after all single iterations in step S34 a ;

[0019] S42, calculate the fitness change value f a -f a-1 , and compare the calculated result with the fitness threshold b until f a -f a-1When b, extract the current iteration number and the optimal population individual in this iteration, extract the control parameters in the optimal population individual as the optimal control parameters of the waste gas denitrification system, and use the optimal control parameters as the control parameter adjustment value of the waste gas denitrification system.

[0020] The technical effects of the present invention are as follows:

[0021] A ship waste gas denitrification optimization control method based on big data is constructed. The present invention collects the control parameters of the waste gas denitrification system and regards the set of control parameters as population individuals; preset the initial parameters of the genetic algorithm, and the initial parameters include the set number, crossover probability, mutation probability and maximum number of iterations, and output the initial population; perform iterative evolution on the initial population until the maximum number of iterations is reached, and output the population individual with the highest fitness as the optimal population individual; use the control parameters in the optimal population individual as the control parameter adjustment value of the waste gas denitrification system. In a complex and variable environment, it can globally optimize the control parameters and avoid falling into local optimal solutions, thereby improving the overall performance of the denitrification system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects and advantages of the present invention will become more obvious:

[0023] Figure 1 It is a schematic flow chart of the ship waste gas denitrification optimization control method according to Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The present invention aims to propose a ship waste gas denitrification optimization control method based on big data. The present invention collects the control parameters of the waste gas denitrification system and regards the set of control parameters as population individuals; preset the initial parameters of the genetic algorithm, and the initial parameters include the set number, crossover probability, mutation probability and maximum number of iterations, and output the initial population; perform iterative evolution on the initial population until the maximum number of iterations is reached, and output the population individual with the highest fitness as the optimal population individual; use the control parameters in the optimal population individual as the control parameter adjustment value of the waste gas denitrification system. In a complex and variable environment, it can globally optimize the control parameters and avoid falling into local optimal solutions, thereby improving the overall performance of the denitrification system.

[0025] Embodiment 1:

[0026] This embodiment proposes a ship waste gas denitrification optimization control method based on big data, as Figure 1 shown, including the following specific steps:

[0027] S1. Collect the control parameters of the waste gas denitrification system and regard the set of control parameters as population individuals;

[0028] S2. Preset initial parameters of the genetic algorithm, which include the number of sets, crossover probability, mutation probability and maximum number of iterations, and output the initial population;

[0029] S3, iteratively evolve the initial population until the maximum number of iterations is reached, and output the population individual with the highest fitness as the optimal population individual;

[0030] S4. Using the control parameters in the optimal population individuals as control parameter adjustment values ​​of the exhaust gas denitrification system.

[0031] In this embodiment, the control parameters of the exhaust gas denitrification system in S1 specifically include: ammonia injection amount, reaction temperature, catalyst activity, ambient humidity, and ship speed.

[0032] In this embodiment, the specific content of S2 is: extracting the control parameter set of the exhaust gas denitrification system collected in S1 as a population individual; presetting the initial parameters of the genetic algorithm, the initial parameters including the number of sets, crossover probability, mutation probability and maximum number of iterations, and outputting the initial population {X1, X2, ..., X n}, where X i is the i-th population individual in the initial population.

[0033] In this embodiment, S3 includes the following specific steps:

[0034] S31, iteratively evolve the initial population, and obtain the fitness value f(X i ), f(X i )=α×N(X i )+β×η(X i ), where N(X i ) is the control parameter set X i Nitrogen-containing gas emissions under i ) is the control parameter set X i The exhaust gas denitrification efficiency under the above conditions, α and β are important coefficients;

[0035] S32, select two population individuals to perform genetic crossover operation to generate new individuals. The formula for generating new individuals is: X ij =αX i +(1-α)X j ;X ij X is a population individual i With X jThe newly generated individual, with the size of α ranging from 0 to 1; calculate the fitness value of the newly generated individual through iteration, and after arranging the fitness values of the individuals in the (a - 1)-th generation population and the individuals in the a-th generation population in descending order, extract the n individuals with the highest fitness values as the new population. Record the individual with the highest fitness value in the new population after a single iteration as the optimal population individual, and record the fitness value f of the optimal population individual. a , where a is the number of iterations, and f a is the fitness value of the optimal population individual after the a-th iteration;

[0036] S33. Repeat the above steps until the number of iterations reaches the preset maximum number of iterations.

[0037] In this embodiment, S4 includes the following specific steps:

[0038] S41. Preset a fitness threshold b, and extract the fitness value f of all the optimal population individuals after each single iteration in step S34 a ;

[0039] S42. Calculate the fitness change value f a - f a-1 , and compare the calculation result with the fitness threshold b until f a - f a-1 < b, then extract the current number of iterations and the optimal population individual in this iteration, extract the control parameters in the optimal population individual as the optimal control parameters of the waste gas denitrification system, and use the optimal control parameters as the control parameter adjustment value of the waste gas denitrification system.

[0040] Embodiment 2:

[0041] This embodiment provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory; the processor executes the above-mentioned optimization control method for ship waste gas denitrification based on big data by calling the computer program stored in the memory.

[0042] This electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) and one or more memories. Among them, at least one computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the optimization control method for ship waste gas denitrification based on big data provided by the above method embodiment. This electronic device can also include other components for implementing the functions of the device. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.

[0043] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), and it can also be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0044] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0045] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and boxes Figure 1 A step that specifies a function in one or more boxes.

[0047] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. The optimization control method of ship exhaust gas denitrification based on big data is characterized by: The specific steps include: S1. Collect the control parameters of the exhaust gas denitrification system and regard the set of control parameters as a population individual; S2. Preset initial parameters of the genetic algorithm, which include the number of sets, crossover probability, mutation probability and maximum number of iterations, and output the initial population; S3, iteratively evolve the initial population until the maximum number of iterations is reached, and output the population individual with the highest fitness as the optimal population individual; S4. Using the control parameters in the optimal population individuals as control parameter adjustment values ​​of the exhaust gas denitrification system.

2. The ship exhaust gas denitrification optimization control method based on big data according to claim 1 is characterized by: The control parameters of the exhaust gas denitrification system in S1 specifically include: ammonia injection amount, reaction temperature, catalyst activity, ambient humidity, and ship speed.

3. The ship exhaust gas denitrification optimization control method based on big data according to claim 2 is characterized by: The specific content of S2 is: extracting the control parameter set of the exhaust gas denitrification system collected in S1 as a population individual; presetting the initial parameters of the genetic algorithm, which include the number of sets, crossover probability, mutation probability and maximum number of iterations, and outputting the initial population {X1, X2, ..., X n }, where X i is the i-th population individual in the initial population.

4. The ship exhaust gas denitrification optimization control method based on big data according to claim 3 is characterized by: The S3 includes the following specific steps: S31, iteratively evolve the initial population, and obtain the fitness value f(X i ), f(X i )=α×N(X i )+β×η(X i ), where N(X i ) is the control parameter set X i Nitrogen-containing gas emissions under i ) is the control parameter set X i The exhaust gas denitrification efficiency under the above conditions, α and β are important coefficients; S32, select two population individuals to perform genetic crossover operation to generate new individuals. The formula for generating new individuals is: X ij =αX i +(1-α)X j ;X ij X is a population individual i With X j The size of α for the generated new individuals is 0-1; Calculate the fitness value of the iteratively generated new individuals, and sort the fitness values ​​of the a-1 generation population individuals and the a generation population individuals in descending order, then extract the n population individuals with the highest fitness values ​​as the new population, record the population individual with the highest fitness in the new population after a single iteration as the optimal population individual, and record the fitness value f of the optimal population individual a , where a is the number of iterations, f a is the fitness value of the optimal population individual after the ath iteration; S33, repeat the above steps until the number of iterations reaches the preset maximum number of iterations.

5. The ship exhaust gas denitrification optimization control method based on big data according to claim 4 is characterized by: The S4 comprises the following specific steps: S41, preset fitness threshold b, extract the fitness value f of the optimal population individual after all single iterations in step S34 a ; S42. Calculate the fitness change value f a -f a-1 , and compare the calculation result with the fitness threshold b until f a -f a-1 < b, extract the current iteration number and the optimal population individuals in this iteration, extract the control parameters in the optimal population individuals as the optimal control parameters of the waste gas denitrification system, and use the optimal control parameters as the control parameter adjustment value of the waste gas denitrification system.

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