A method for energy efficiency optimization of a manufacturing system considering multi-dimensional uncertainty

Abstract

The application discloses a kind of manufacturing system energy efficiency optimization methods considering multidimensional uncertainty, belongs to energy-saving technology field, and its technical key points are: including the following steps: step one: the uncertainty model of manufacturing system, the reliability of manufacturing system is quantified using Markov method, the probability of each state of multi-state system in a particular period is obtained, and the state of manufacturing system is defined as "working state" and "shutdown state";Step two: robustness optimization, robust optimization is carried out on the energy consumption of manufacturing system to realize the minimization of energy consumption, with the total energy consumption of manufacturing system as the optimization goal, the total energy consumption of manufacturing system is minimized by optimizing process parameters under the premise of ensuring production quality and normal operation.

Description

Technical Field

[0001] This invention relates to the field of energy conservation and consumption reduction technology, specifically to a method for optimizing the energy efficiency of a manufacturing system that considers multidimensional uncertainties. Background Technology

[0002] With environmental degradation and rising energy prices, reducing carbon emissions and improving energy efficiency have become global hot issues, attracting increasing attention. Currently, global energy supply mainly comes from carbon-based resources. Global energy consumption has increased by 25% in the last 20 years and is projected to increase by 15-35% by 2030. Manufacturing will consume one-third of global primary energy and directly or indirectly generate over 38% of carbon dioxide emissions. Statistics show that my country's rapidly developing manufacturing sector accounts for nearly 60% of the country's total energy consumption. Numerous surveys indicate that energy efficiency in manufacturing processes is very low. In recent years, due to stringent environmental regulations, increased customer environmental awareness, rising energy prices, and growing concerns about resource and energy shortages and climate change, manufacturing companies have recognized the importance of optimizing energy efficiency.

[0003] Energy efficiency in enterprise production is considered a guarantee of future global corporate competitiveness. Enterprises can reduce energy consumption to cope with fierce market competition and avoid adverse environmental impacts by raising awareness of energy conservation and optimizing production operations. By controlling energy consumption, manufacturing enterprises can not only reduce the environmental impact of carbon emissions but also lower the currently rapidly increasing energy costs. Therefore, reducing energy consumption and improving energy efficiency have become top priorities for companies, and lowering energy costs is crucial for maintaining competitiveness. Manufacturing enterprises are striving to reduce system energy consumption to improve sustainability. The energy efficiency of manufacturing processes has attracted widespread interest from academia and industry, and energy efficiency in sustainable production is widely recognized as a key lever for enhancing future global corporate competitiveness. During industrial transformation and upgrading, low-carbon manufacturing aimed at improving energy utilization efficiency will become an important means and pathway for my country's manufacturing industry to achieve energy conservation and emission reduction, and has become a new issue and challenge facing my country's manufacturing industry. How to reduce the energy consumption level of manufacturing systems and improve energy utilization efficiency has become an urgent need.

[0004] In a manufacturing system, various pieces of equipment coordinate and work together to complete the production process from raw materials to high-quality finished products. If one component malfunctions, the entire manufacturing system cannot operate normally. The uncertainties in the various equipment states during the production process—such as shutdowns, standby, preheating, and processing—as well as the uncertainties inherent in the hardware and control software systems of equipment from different manufacturers, will severely impact the accuracy and effectiveness of energy consumption optimization. Furthermore, the various process parameters in a manufacturing system are interconnected and coupled. A change in one process parameter may cause changes in other related process parameters, making the production process complex. The interaction between the uncertainties of process parameters will also affect the accuracy and effectiveness of manufacturing system energy optimization.

[0005] The energy consumption uncertainties in a manufacturing system can be summarized into three dimensions: demand, production, and energy. These multidimensional uncertainties increase the difficulty of energy consumption optimization.

[0006] There is a lack of existing research literature on system-level energy efficiency optimization of manufacturing systems, and the few existing energy efficiency optimization studies are steady-state and do not take into account the large number of dynamic uncertainties in the production process. Therefore, it is necessary to optimize the relevant process parameters of the manufacturing system based on multidimensional uncertainties and establish corresponding energy efficiency optimization models. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method for optimizing the energy efficiency of a manufacturing system that considers multidimensional uncertainties, so as to solve the problems in the background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] An energy efficiency optimization method for manufacturing systems considering multidimensional uncertainties includes the following steps:

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

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

[0012] As a further aspect of the present invention, the Markov method includes:

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

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

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

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

[0017] As a further aspect of the present invention, the probability of each state of the manufacturing system at time t in step one 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 of the manufacturing system in state i.

[0020] As a further aspect of the present invention, 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.

[0021] P(∞)=P(∞-1)A=P(∞)A (5).

[0022] As a further aspect of the present invention, the reliability metric is defined as the ratio of "downtime" to "operational status". The manufacturing system will experience "downtime" of varying severity and corresponding repair times.

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

[0024]

[0025] Where t represents the manufacturing system running time; i represents the aluminum profile product category; This indicates the processing time of product i on machine j; This represents the energy consumption per unit time when product i is processed on machine j; This indicates the standby time of product i on machine j; This represents the energy consumption of machine j per unit of standby time. This represents the preprocessing time when machine j changes to product i. This represents the energy consumption per unit time during preprocessing when machine j changes to product i; Indicates that the product comes from machine M j-1 To M j Transportation time between; Indicates that the product comes from machine Mj-1 To M j Energy consumption per unit mass of transport; m i This indicates the quality of product i.

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

[0027] i: The manufacturing system equipment is simulated through a simulation program;

[0028] ii: Identified existing uncertainties for use in manufacturing system equipment load calculations;

[0029] iii: The identified variable samples are general and are 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: Given that the maintenance rate and failure rate of the equipment in the manufacturing system 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 load distribution of the manufacturing system equipment is known, the energy consumption of the manufacturing system in each state is evaluated under a given capacity condition. The total energy consumption of the manufacturing system is obtained by summing up the energy consumption of the manufacturing system in each state and the steady-state probability in that state.

[0032] vi: Minimize the total energy consumption of the manufacturing system by changing the system capacity, thereby achieving robust optimization of the manufacturing system's energy consumption.

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

[0034] This invention utilizes the advantages of robust optimization methods in handling uncertain parameters in optimization problems, and considers multidimensional uncertainties in the problem of energy consumption optimization in manufacturing systems;

[0035] Based on the multidimensional uncertainties of the manufacturing system, the main process parameters affecting the energy consumption of the manufacturing system are optimized to minimize the total energy consumption of the manufacturing system while ensuring production quality and normal operation.

[0036] To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0037] Figure 1 This is a robust energy consumption optimization process for a manufacturing system according to an embodiment of the invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0039] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0040] In one embodiment, a method for optimizing the energy efficiency of a manufacturing system considering multidimensional uncertainties is described in [reference needed]. Figure 1 This includes 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 "downtime state".

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

[0043] Further, 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 based on the failure rate and maintenance rate of equipment in the manufacturing system;

[0046] IIi: The probability of obtaining each state of the manufacturing system;

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

[0048] Further, see Figure 1 In step one, the probability of each state of the manufacturing system at time t 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 of the manufacturing system in state i.

[0051] Further, see Figure 1 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.

[0052] P(∞)=P(∞-1)A=P(∞)A (5).

[0053] Further, see Figure 1 The reliability metric is defined as the ratio of "downtime" to "operational time". The manufacturing system will experience "downtime" of varying severity and corresponding repair times.

[0054] Further, see Figure 1 The manufacturing system in step two can function as follows:

[0055]

[0056] Where t represents the manufacturing system running time; i represents the aluminum profile product category; This indicates the processing time of product i on machine j; This represents the energy consumption per unit time when product i is processed on machine j; This indicates the standby time of product i on machine j; This represents the energy consumption of machine j per unit of standby time. This represents the preprocessing time when machine j changes to product i. This represents the energy consumption per unit time during preprocessing when machine j changes to product i; Indicates that the product comes from machine M j-1 To M j Transportation time between; Indicates that the product comes from machine M j-1 To M j Energy consumption per unit mass of transport; m i This indicates the quality of product i.

[0057] Further, see Figure 1 The robust optimization process in step two includes:

[0058] i: The manufacturing system equipment is simulated through a simulation program;

[0059] Ii: Identifies existing uncertainties for use in manufacturing system equipment load calculations;

[0060] iii: The identified variable samples are general and are 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: Given that the maintenance rate and failure rate of the equipment in the manufacturing system 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 load distribution of the manufacturing system equipment is known, the energy consumption of the manufacturing system in each state is evaluated under a given capacity condition. The total energy consumption of the manufacturing system is obtained by summing up the energy consumption of the manufacturing system in each state and the steady-state probability in that state.

[0063] vi. To achieve robust optimization of manufacturing system energy consumption, the total energy consumption of the manufacturing system can be minimized by changing the system capacity.

[0064] In this embodiment, the present invention employs the Markov method to quantify the reliability of the manufacturing system. This method obtains the probability of each state of a multi-state system over a specific period, and this information is used to estimate the system's reliability. Specifically, the manufacturing system states are defined as "operating states" and "downtime states," and the reliability metric is defined as the ratio of "downtime states" to "operating states." The manufacturing system will experience "downtime states" of varying severity and corresponding 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 based on the failure rate and maintenance rate of 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 represented by a vector P(t) (Equation (1)), where k is the total number of states. i (t) is the probability of the manufacturing system 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 approaches 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. Equation (8) can be used to calculate the average steady-state energy consumption (E) of the manufacturing system. ∞ ) to conduct an evaluation, in which g iIt is the energy consumption of the manufacturing system in state i, from which the defects caused by the failure 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] Robust optimization of the manufacturing system's energy consumption is performed to minimize 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 manufacturing system running time; i represents the aluminum profile product category; This indicates the processing time of product i on machine j; This represents the energy consumption per unit time when product i is processed on machine j; This indicates the standby time of product i on machine j; This represents the energy consumption of machine j per unit of standby time. This represents the preprocessing time when machine j changes to product i. This represents the energy consumption per unit time during preprocessing when machine j changes to product i; Indicates that the product comes from machine M j-1 To M j Transportation time between; Indicates that the product comes from machine M j-1 To M j Energy consumption per unit mass of transport; m i This indicates the quality of product i.

[0081] By quantifying the uncertainty and reliability of a manufacturing system, robust optimization can be used to minimize the total energy consumption of the manufacturing system. For example... Figure 1 As shown, the detailed steps are as follows:

[0082] i. The manufacturing system equipment is simulated through a simulation program.

[0083] ii. Existing uncertainties were identified for use in manufacturing system equipment load calculations.

[0084] iii. The identified variable samples are general and are imported into the simulation program. For variables with uncertainties, the sampling method used may differ. The Monte Carlo method can be used to obtain a load diagram of the manufacturing system equipment considering uncertainties.

[0085] iv. Given that the maintenance rate and failure rate of the equipment in the manufacturing system are known, the steady-state probability of the manufacturing system in each state can be obtained using the Markov method.

[0086] v. Given a known load distribution of equipment in a manufacturing system, the energy consumption of the system in each state can be evaluated under a given capacity. By summing the energy consumption of the manufacturing system in each state and the steady-state probability at that state, the total energy consumption of the manufacturing system can be obtained.

[0087] vi. To achieve robust optimization of manufacturing system energy consumption, the total energy consumption of the manufacturing system can be minimized by changing the system capacity.

[0088] The working principle of this invention is:

[0089] A robust optimization method suitable for uncertainty is adopted. Based on the main uncertain factors affecting the energy consumption of the manufacturing system, the total energy consumption of the manufacturing system is taken as the optimization objective. Under the premise of ensuring production quality and normal operation, the total energy consumption of the manufacturing system is minimized by optimizing process parameters.

[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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for energy efficiency optimization of a manufacturing system considering multi-dimensional uncertainty, characterized in that, The method comprises the following steps: Step 1: manufacturing system uncertainty model, using Markov method to quantify the reliability of manufacturing system, to obtain the probability of each state of the multi-state system in a specific period, the manufacturing system state is defined as "working state" and "shutdown state"; Step 2: robust optimization, robust optimization of manufacturing system energy consumption to achieve the minimization of energy consumption; The Markov method comprises: i: list all possible states of the manufacturing system; ii: determine the state transition density matrix determined by the failure rate and repair 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; The reliability measure is defined as the ratio of "shutdown state" to "working state", and the manufacturing system will appear different severity of "shutdown state" and the corresponding different repair time; The robust optimization process in step 2 comprises: i: manufacturing system equipment is simulated by simulation program; ii: identify the existing uncertainty variables for manufacturing system equipment load calculation; iii: the identified variable sample is universal and is imported into the simulation program, and the load diagram of the manufacturing system equipment considering uncertainty is obtained by using Monte Carlo method for the variable with uncertainty; iv: under the condition that the repair rate and failure rate of the manufacturing system equipment are known, the steady-state probability of the manufacturing system in each state can be obtained by using Markov method; v: under the condition that the load distribution of the manufacturing system equipment is known, the energy consumption of the manufacturing system in each state is evaluated under the condition of a given capacity, and 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: by changing the system capacity to minimize the total energy consumption of the manufacturing system, the robust optimization of the manufacturing system energy consumption is realized.

2. The method for energy efficiency optimization of a manufacturing system considering multi-dimensional uncertainty according to claim 1, wherein, The probability of each state of the manufacturing system at time t in step 1 can be represented by vector P(t), （1） where k is the total number of states, p i (t) is the probability of the manufacturing system being in state i at time t.

3. The method for energy efficiency optimization of a manufacturing system considering multi-dimensional uncertainty according to claim 2, wherein, The vector P(0) is the initial state of the manufacturing system, and when the time tends to infinity, the state of the manufacturing system will remain stable, and the vector P(∞) will approach a constant; （5）。 4. The method for energy efficiency optimization of a manufacturing system considering multi-dimensional uncertainty according to claim 1, wherein, The manufacturing system in step 2 can be as follows: where t denotes the manufacturing system run time; i denotes the aluminium profile product class; denotes the processing time of product i on machine j; denotes the energy consumption per time unit of product i processing on machine j; denotes the standby time of product i on machine j; denotes the energy consumption per time unit of machine j standby; denotes the pre-processing time of machine j changeover for product i; denotes the energy consumption per time unit of machine j pre-processing for product i changeover; denotes the transport time of product from machine M j-1 to M j ; denotes the energy consumption per mass of product transport from machine M j-1 to M j ; m i denotes the mass of product i.

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