Manufacturing system energy efficiency optimization method considering multi-dimensional uncertainty

Abstract

The invention discloses a manufacturing system energy efficiency optimization method considering multi-dimensional uncertainty, which belongs to the technical field of energy conservation and consumption reduction, and is technically characterized by comprising the following steps of: 1, quantifying the reliability of a manufacturing system by adopting a Markov method according to an uncertainty model of the manufacturing system, the probability of each state of the multi-state system in a specific period is obtained, and the states of the manufacturing system are defined as a working state and a shutdown state; 2, robustness optimization is carried out, robust optimization is carried out on the energy consumption of the manufacturing system to achieve minimization of the energy consumption, and the method has the advantages that the total energy consumption of the manufacturing system serves as an optimization target, and minimization of the total energy consumption of the manufacturing system is achieved by optimizing technological parameters on the premise that the production quality and normal operation are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of energy saving and consumption reduction, and in particular to a manufacturing system energy efficiency optimization method taking multi-dimensional uncertainty into consideration. Background Art

[0002] With the deterioration of the environment and the rise in energy prices, reducing carbon emissions and improving energy efficiency have become global hot issues and have received increasing attention. At present, the global energy supply mainly comes from carbon-based resources. In the past 20 years, global energy consumption has increased by 25%, and it is expected to increase by 15-35% before 2030. The manufacturing industry will consume one-third of the world's primary energy and directly or indirectly produce more than 38% of carbon dioxide. According to statistics, my country's rapidly developing manufacturing industry has led to the energy consumption of my country's manufacturing industry approaching 60% of my country's total energy consumption. A large number of surveys have shown that the energy efficiency of the manufacturing process is very low. In recent years, due to the influence of strict environmental regulations, customer environmental awareness and rising energy prices, and due to increasing concerns about resource and energy supply shortages and climate change, manufacturing companies have realized the importance of energy efficiency optimization.

[0003] Energy efficiency in enterprise production is considered to be the guarantee of future global enterprise competitiveness. Enterprises can reduce energy consumption through energy-saving and consumption-reduction awareness and optimization of production operations to cope with fierce market competition and avoid adverse effects on the environment. By controlling energy consumption, production enterprises can not only reduce the impact of carbon emissions on the environment, but also reduce the current sharply increasing energy costs. Therefore, reducing energy consumption and improving energy efficiency have become the top priorities of companies, and reducing energy costs is important for maintaining competitiveness. In order to improve sustainability, production enterprises are working hard to reduce system energy consumption. The issue of energy efficiency in the manufacturing production process has aroused widespread interest in academia and industry. Energy efficiency in sustainable production of enterprises is recognized as an important lever for improving the competitiveness of global enterprises in the future. In the process of industrial transformation and upgrading, low-carbon manufacturing with the purpose of improving energy utilization efficiency will become an important means and way for my country's manufacturing industry to achieve energy conservation and emission reduction, and has become a new topic and new challenge facing my country's manufacturing industry. How to reduce the energy consumption level of the manufacturing system and improve energy utilization efficiency has become an urgent need at present.

[0004] In the manufacturing system, various equipments coordinate and cooperate to complete the production process from raw materials to high-quality finished products. If one component fails, the entire manufacturing system cannot operate normally. The uncertainty of various equipment states such as shutdown, standby, preheating, and processing in the production process of the manufacturing system, as well as the uncertainty of equipment hardware and control software systems from different manufacturers, will also seriously affect the accuracy and effectiveness of energy consumption optimization. The various process parameters of the manufacturing system are interrelated and coupled with each other. The change of one process parameter may cause changes in other related process parameters, making the production process complicated. The interaction between the uncertainty of process parameters will also affect the accuracy and effectiveness of the optimization of the manufacturing system.

[0005] The energy consumption uncertainty factors in the production process of the manufacturing system can be summarized into three dimensions: demand dimension, production dimension and energy dimension. The multi-dimensional uncertainty factors of energy consumption in the production process of the manufacturing system make it more difficult to optimize energy consumption.

[0006] There are few research papers on system-level energy efficiency optimization of existing manufacturing systems, and the few energy efficiency optimization studies are steady-state and do not consider the large number of dynamic uncertainty factors in the production process. Therefore, it is necessary to optimize the relevant process parameters of the manufacturing system based on multi-dimensional uncertainty and establish a corresponding energy efficiency optimization model. Summary of the invention

[0007] In view of the shortcomings of the prior art, an embodiment of the present invention aims to provide a method for optimizing energy efficiency of a manufacturing system taking multi-dimensional uncertainties into consideration, so as to solve the problems in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for optimizing energy efficiency of a manufacturing system considering multi-dimensional uncertainty comprises the following steps:

[0010] Step 1: Uncertainty model of manufacturing system. Use Markov method to quantify the reliability of manufacturing system, obtain the probability of each state of multi-state system in a specific period, and define the state of manufacturing system as "working state" and "shutdown state";

[0011] Step 2: Perform robust optimization to optimize the energy consumption of the manufacturing system to minimize the energy consumption.

[0012] As a further solution of the present invention, the Markov method comprises:

[0013] i: List all possible states of the manufacturing system;

[0014] ii: Determine the state transition density matrix determined by the failure rate and maintenance rate of the equipment in the manufacturing system;

[0015] iii: obtain the probability of each state of the manufacturing system;

[0016] iv: Calculate the average steady-state performance of the manufacturing system.

[0017] As a further solution of the present invention, the probability of each state of the manufacturing system at time t in step 1 can be represented by a vector P(t),

[0018] P(t)=[p1(t),p2(t)...p k (t)] T (1)

[0019] Where k is the total number of states, p i (t) is the probability that the manufacturing system is in state i.

[0020] As a further solution of the present invention, the vector P(0) is the initial state of the manufacturing system. When time tends to infinity, the state of the manufacturing system will remain stable, and the vector P(∞) will approach a constant.

[0021] P(∞)=P(∞-1)A=P(∞)A (5).

[0022] As a further solution of the present invention, the reliability metric is defined as the ratio of "downtime" to "working status", and the manufacturing system will have "downtime" of different severity and corresponding different repair times.

[0023] As a further solution of the present invention, the manufacturing system in step 2 can be as follows:

[0024]

[0025] Where t represents the operating time of the manufacturing system; i represents the aluminum profile product category; represents the processing time of product i on machine j; represents the energy consumption per unit time when product i is processed on machine j; represents the standby time of product i on machine j; represents the standby energy consumption per unit time of machine j; represents the preprocessing time when machine j changes product i; It represents the energy consumption per unit time of pre-processing when machine j changes product i; Indicates that the product is from machine M j-1 To M j The transportation time between Indicates that the product is from machine Mj-1 To M j Unit mass transport energy consumption; m i Represents the quality of product i.

[0026] As a further solution of the present invention, the robust optimization process in step 2 includes:

[0027] i: Manufacturing system equipment is simulated through simulation programs;

[0028] ii: Identified the existing uncertain variables for use in manufacturing system equipment load calculation;

[0029] iii: The identified variable samples are universal and imported into the simulation program. For variables with uncertainties, the Monte Carlo method is used to obtain the load diagram of the manufacturing system equipment considering the uncertainties;

[0030] iv: When the maintenance rate and failure rate of the manufacturing system equipment are known, the Markov method can be used to obtain the steady-state probability of the manufacturing system in each state;

[0031] v: Under the condition that the equipment load distribution of the manufacturing system is known, the energy consumption of the manufacturing system in each state is evaluated under the condition of a given capacity. The total energy consumption of the manufacturing system is obtained by summarizing the energy consumption of the manufacturing system in each state and the steady-state probability in this state;

[0032] vi: Minimize the total energy consumption of the manufacturing system by varying the system capacity to achieve robust optimization of the energy consumption of the manufacturing system.

[0033] In summary, compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0034] The present invention takes advantage of the robust optimization method to handle uncertain parameters in optimization problems and considers multi-dimensional uncertainty in the problem of optimizing energy consumption in manufacturing systems;

[0035] On the basis of considering the multi-dimensional uncertainty of the manufacturing system, the main process parameters that affect the energy consumption of the manufacturing system are optimized, and the total energy consumption of the manufacturing system is minimized while ensuring production quality and normal operation.

[0036] In order to more clearly illustrate the structural features and effects of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A robust optimization process for energy consumption of a manufacturing system according to an embodiment of the invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0039] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0040] In one embodiment, a method for optimizing energy efficiency of a manufacturing system considering multi-dimensional uncertainty is described. Figure 1 , including the following steps:

[0041] Step 1: Uncertainty model of manufacturing system. Use Markov method to quantify the reliability of manufacturing system, obtain the probability of each state of multi-state system in a specific period, and define the state of manufacturing system as "working state" and "shutdown state";

[0042] Step 2: Perform robust optimization to optimize the energy consumption of the manufacturing system to minimize the energy consumption.

[0043] For further information, see Figure 1 , the Markov method includes:

[0044] i: List all possible states of the manufacturing system;

[0045] Ii: Determine the state transition density matrix determined by the failure rate and maintenance rate of the equipment in the manufacturing system;

[0046] Iii: Obtain the probability of each state of the manufacturing system;

[0047] Iv: Calculate the average steady-state performance of the manufacturing system.

[0048] For further information, see Figure 1 , the probability of each state of the manufacturing system at time t in step 1 can be represented by a vector P(t),

[0049] P(t)=[p1(t),p2(t)...p k (t)] T (1)

[0050] Where k is the total number of states, p i (t) is the probability that the manufacturing system is in state i.

[0051] For further information, see Figure 1 , the vector P(0) is the initial state of the manufacturing system. When time tends to infinity, the state of the manufacturing system will remain stable, and the vector P(∞) will approach a constant;

[0052] P(∞)=P(∞-1)A=P(∞)A (5).

[0053] For further information, see Figure 1 The reliability metric is defined as the ratio of “downtime state” to “working state”. The manufacturing system will have “downtime states” of different severity and corresponding different repair times.

[0054] For further information, see Figure 1 , the manufacturing system in step 2 can be as follows:

[0055]

[0056] Where t represents the operating time of the manufacturing system; i represents the aluminum profile product category; represents the processing time of product i on machine j; represents the energy consumption per unit time when product i is processed on machine j; represents the standby time of product i on machine j; represents the standby energy consumption per unit time of machine j; represents the preprocessing time when machine j changes product i; It represents the energy consumption per unit time of pre-processing when machine j changes product i; Indicates that the product is from machine M j-1 To M j The transportation time between Indicates that the product is from machine M j-1 To M j Unit mass transport energy consumption; m i Represents the quality of product i.

[0057] For further information, see Figure 1 , the robust optimization process in step 2 includes:

[0058] i: Manufacturing system equipment is simulated through simulation programs;

[0059] Ii: The existing uncertain variables are identified for use in manufacturing system equipment load calculation;

[0060] iii: The identified variable samples are universal and imported into the simulation program. For variables with uncertainties, the Monte Carlo method is used to obtain the load diagram of the manufacturing system equipment considering the uncertainties;

[0061] iv: When the maintenance rate and failure rate of the manufacturing system equipment are known, the Markov method can be used to obtain the steady-state probability of the manufacturing system in each state;

[0062] v: Under the condition that the equipment load distribution of the manufacturing system is known, the energy consumption of the manufacturing system in each state is evaluated under the condition of a given capacity. The total energy consumption of the manufacturing system is obtained by summarizing the energy consumption of the manufacturing system in each state and the steady-state probability in this state;

[0063] vi. Minimize the total energy consumption of the manufacturing system by varying the system capacity to achieve robust optimization of the energy consumption of the manufacturing system.

[0064] In this embodiment, the present invention uses the Markov method to quantify the reliability of the manufacturing system, which can obtain the probability of each state of the multi-state system in a specific period, and this information is used to estimate the reliability of the system. That is, the manufacturing system state is defined as "working state" and "downtime state", and the reliability measure is defined as the ratio of "downtime state" to "working state". The manufacturing system will have "downtime states" of different severity and corresponding different repair times.

[0065] The steps of the Markov method are as follows:

[0066] i. List all possible states of the manufacturing system;

[0067] ii. Determine the state transition density matrix determined by the failure rate and maintenance rate of the equipment in the manufacturing system;

[0068] iii. Obtain the probability of each state of the manufacturing system;

[0069] iv. Calculate the average steady-state performance of the manufacturing system.

[0070] The probability of each state of the manufacturing system at time t can be expressed by the vector P(t) (Equation (1)), where k is the total number of states. i (t) is the probability of the manufacturing system being in state i, which can be derived from the initial state according to equations (2)-(4), where A (Equation (3)) is the state transition density matrix determined by the failure rate (λ) and repair rate (μ) of the equipment in the manufacturing system, and its determination is a key issue. P(0) is the initial state of the manufacturing system. When time tends to infinity, the state of the manufacturing system will remain stable, and the vector P(∞) will approach a constant (Equation (5)). Equation (6) can be obtained by transforming Equation (5). Equation (7) shows that the sum of the probabilities of all states is 1. By solving the system of linear algebraic equations (Equations (6) and (7)), the steady-state probability vector P(∞) can be obtained. The average steady-state energy consumption (E) of the manufacturing system can be calculated using Equation (8). ∞ ) is evaluated, where g iis the energy consumption of the manufacturing system in state i, from which the defects caused by failures can be calculated. When the two variables, failure rate (λ) and repair rate (μ), are considered to be time independent, they can be calculated by equations (9)-(10) [3.2]. Among them, MTTF is the "mean time to failure" and MTTR is the "mean time to repair".

[0071] P(t)=[p1(t),p2(t)...p k (t)] T (1)

[0072] P(1)=P(0)A (2)

[0073]

[0074] P(m)=P(m-1)A=P(0)A m (4)

[0075] P(∞)=P(∞-1)A=P(∞)A (5)

[0076] P(∞)(AI)=0 (6)

[0077]

[0078] The energy consumption of the manufacturing system is robustly optimized to minimize the energy consumption. The total energy consumption E of the aluminum profile extrusion manufacturing system is shown in equation (11):

[0079]

[0080] Where t represents the operating time of the manufacturing system; i represents the aluminum profile product category; represents the processing time of product i on machine j; represents the energy consumption per unit time when product i is processed on machine j; represents the standby time of product i on machine j; represents the standby energy consumption per unit time of machine j; represents the preprocessing time when machine j changes product i; It represents the energy consumption per unit time of pre-processing when machine j changes product i; Indicates that the product is from machine M j-1 To M j The transportation time between Indicates that the product is from machine M j-1 To M j Unit mass transport energy consumption; m i Represents the quality of product i.

[0081] By quantifying the uncertainty and reliability of the manufacturing system, the total energy consumption of the manufacturing system can be minimized through robust optimization. Figure 1 The detailed steps are as follows:

[0082] i. Manufacturing system equipment is simulated through simulation programs.

[0083] ii. The existing uncertain variables are identified for use in manufacturing system equipment load calculations.

[0084] iii. The sample of variables identified is generic and imported into the simulation program. For variables with uncertainties, the sampling method used may be different. The load diagram of the manufacturing system equipment considering the uncertainties can be obtained using the Monte Carlo method.

[0085] iv. When the maintenance rate and failure rate of the manufacturing system equipment are known, the Markov method can be used to obtain the steady-state probability of the manufacturing system in each state.

[0086] v. When the equipment load distribution of the manufacturing system is known, the energy consumption of the manufacturing system in each state can be evaluated under a given capacity. The total energy consumption of the manufacturing system can be obtained by summarizing the energy consumption of the manufacturing system in each state and the steady state probability in this state.

[0087] vi. Minimize the total energy consumption of the manufacturing system by varying the system capacity to achieve robust optimization of the energy consumption of the manufacturing system.

[0088] The working principle of the present invention is:

[0089] An optimization method suitable for uncertainty (robust optimization) is adopted. Based on the main uncertainty influencing factors of the energy consumption of the manufacturing system, the total energy consumption of the manufacturing system is taken as the optimization target. The total energy consumption of the manufacturing system is minimized by optimizing the process parameters while ensuring production quality and normal operation.

[0090] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing energy efficiency of a manufacturing system considering multi-dimensional uncertainty, characterized in that: The following steps are involved: Step 1: Uncertainty model of manufacturing system. Use Markov method to quantify the reliability of manufacturing system, obtain the probability of each state of multi-state system in a specific period, and define the state of manufacturing system as "working state" and "shutdown state"; Step 2: Perform robust optimization to optimize the energy consumption of the manufacturing system to minimize the energy consumption.

2. The manufacturing system energy efficiency optimization method considering multi-dimensional uncertainty according to claim 1 is characterized in that: The Markov method includes: i: List all possible states of the manufacturing system; ii: Determine the state transition density matrix determined by the failure rate and maintenance rate of the equipment in the manufacturing system; iii: obtain the probability of each state of the manufacturing system; iv: Calculate the average steady-state performance of the manufacturing system.

3. The manufacturing system energy efficiency optimization method considering multi-dimensional uncertainty according to claim 2 is characterized in that: The probability of each state of the manufacturing system at time t in step 1 can be represented by a vector P(t), P(t)=[p1(t),p2(t)...p k (t)] T (1) Where k is the total number of states, p i (t) is the probability that the manufacturing system is in state i.

4. The manufacturing system energy efficiency optimization method considering multi-dimensional uncertainty according to claim 3 is characterized in that: The vector P(0) is the initial state of the manufacturing system. When time approaches infinity, the state of the manufacturing system will remain stable, and the vector P(∞) will approach a constant. P(∞)=P(∞-1)A=P(∞)A (5).

5. The manufacturing system energy efficiency optimization method considering multi-dimensional uncertainty according to claim 4 is characterized in that: The reliability metric is defined as the ratio of "downtime" to "working status". A manufacturing system will have "downtime" of different severity and corresponding different repair times.

6. The manufacturing system energy efficiency optimization method considering multi-dimensional uncertainty according to claim 1, characterized in that: The manufacturing system in step 2 can be as follows: Where t represents the operating time of the manufacturing system; i represents the aluminum profile product category; represents the processing time of product i on machine j; represents the energy consumption per unit time when product i is processed on machine j; represents the standby time of product i on machine j; represents the standby energy consumption per unit time of machine j; represents the preprocessing time when machine j changes product i; It represents the energy consumption per unit time of pre-processing when machine j changes product i; Indicates that the product is from machine M j-1 To M j The transportation time between Indicates that the product is from machine M j-1 To M j Unit mass transport energy consumption; m i Represents the quality of product i.

7. The manufacturing system energy efficiency optimization method considering multi-dimensional uncertainty according to claim 6 is characterized in that: The robust optimization process in step 2 includes: i: Manufacturing system equipment is simulated through simulation programs; ii: Identified the existing uncertain variables for use in manufacturing system equipment load calculation; iii: The identified variable samples are universal and imported into the simulation program. For variables with uncertainties, the Monte Carlo method is used to obtain the load diagram of the manufacturing system equipment considering the uncertainties; iv: When the maintenance rate and failure rate of the manufacturing system equipment are known, the Markov method can be used to obtain the steady-state probability of the manufacturing system in each state; v: Under the condition that the equipment load distribution of the manufacturing system is known, the energy consumption of the manufacturing system in each state is evaluated under the condition of a given capacity. The total energy consumption of the manufacturing system is obtained by summarizing the energy consumption of the manufacturing system in each state and the steady-state probability in this state; vi: Minimize the total energy consumption of the manufacturing system by varying the system capacity to achieve robust optimization of the energy consumption of the manufacturing system.

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