CN111275260B - Remote production process collaborative optimization system and method - Google Patents
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Abstract
A remote production process collaborative optimization system and a method relate to the technical field of management of chemical industry automation equipment and a digital workshop. The system and the method comprise a collaborative optimization system consisting of a data acquisition platform, a data storage module, an intelligent computing module and a collaborative optimization module; and the distributed deployment is cooperated with a plurality of devices, so that the on-site data is automatically collected and analyzed, and the bottleneck of the production efficiency is timely found. In the production process, the related production parameters are automatically and remotely optimized in a synergic mode through a network by utilizing a TCP/IP protocol, so that the production energy is improved, and the safe and orderly efficient production is ensured.
Description
Technical Field
The invention relates to the technical field of management of automation equipment and a digital workshop in the chemical industry, in particular to a remote production process collaborative optimization system and a method.
Background
In modern enterprises, the application scale of mechanical equipment is larger and larger, on one hand, the working intensity and the environment of personnel are improved, the cost of the personnel is reduced, on the other hand, the degree of automation is improved, and the high-quality and high-efficiency production is ensured. In actual production, whether the equipment production parameter setting reasonably and directly affects the efficiency, in actual operation, staff refers to and resets according to the past production experience or equipment state data, so that artificial deviation is inevitably introduced, the equipment state is affected, the labor cost is increased, problems cannot be found in time, and certain hysteresis is achieved, so that lower equipment productivity and efficiency are caused.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a remote production process collaborative optimization system and a method, and the technical scheme adopted by the invention is as follows: the collaborative optimization system comprises a data acquisition platform, a data storage module, an intelligent computing module and a collaborative optimization module; in the production process, the collaborative optimization system utilizes a TCP/IP protocol to automatically remotely and collaborative optimize related production parameters through a network, thereby improving the production energy and ensuring safe and orderly high-efficiency production;
the data acquisition platform comprises a data acquisition module and a data uploading module, wherein the data acquisition module is used for acquiring and uploading parameter values and production operation values of chemical production equipment. The data storage module is used for storing the acquired parameter values and production operation values of the production equipment. The intelligent computing module is used for acquiring the optimal parameter adjustment value of the production equipment through a step-by-step computing formula according to the data stored in the data storage module, the acquired parameter value and the production operation value of the production equipment. The collaborative optimization module is used for recording and storing the parameter adjustment value, determining the optimal parameter, and transmitting the value to the equipment by utilizing a TCP/IP protocol through a network.
Further, the intelligent computing module comprises the following steps of:
first, through the formula
Calculating a parameter value of the first production facility, wherein V represents volume, unit: l, M represent mass, unit: kg, p stands for material density, unit: kg/L;
second, through the formula
Calculating a first production run value and a second production run value, wherein the first production run value v represents flow rate in units of: l/s, V represents volume, unit: l, the second production run value t represents the filling time in units of: s;
thirdly, calculating the relation between the parameter value of the second production equipment and the parameter value of the third production equipment and the first production operation value through the formula v=alpha.C.P, wherein v represents the flow rate in units: l/s, α represents a correction coefficient, dimensionless, C represents the angle of the second generating device, unit: d, P stands for third production equipment constant, unit: l/(s.d); and calculating the angle C of the second production equipment through real-time data.
Fourth, through the formula
Calculating a third production run value, wherein the third year production run value P (s n ) Representing the qualification rate of the nth batch; fourth production run value s n Representing the number of filling barrels of the nth lot, and the fifth production run value S represents the number of filling barrels of all lots.
Still further, according to the second step, the method is represented by the formula
Calculating the relation between the first production operation value and the second production operation value;
t is less than or equal to t at time 0 1 The flow rate corresponds to equation 4: v=a 1 t+b 1 I.e. the flow rate is increasing. At time t 1 ≤t＜t 2 The flow rate corresponds to equation 5: v=b 2 I.e. the flow rate is constant. At time t 2 ≤t≤t 3 The flow rate corresponds to equation 6: v= -a 3 t+b 3 I.e. the flow rate is continuously decreasing.
Calculating a through real-time data 1 、b 1 、b 2 、a 3 B 3 。a 1 Acceleration, b, representing the flow rate at the start of filling 1 A constant value representing the contemporaneous flow rate increase. b 2 Representing a constant flow rate in the middle of filling. a, a 3 Representing the rate of flow decrease in the later stages of filling, b 3 A constant value representing the decrease in flow rate at the end of filling.
The invention also provides a remote production process collaborative optimization method, which comprises a collaborative optimization system consisting of a data acquisition platform, a data storage module, an intelligent computing module and a collaborative optimization module. The steps are as follows,
s1, parameter values and production operation values of chemical production equipment are acquired and uploaded through the data acquisition platform. S2, storing the acquired parameter values and production operation values of the production equipment through the data storage module. S3, obtaining the optimal parameter adjustment value of the production equipment through the intelligent calculation module according to the data stored in the data storage module, the acquired parameter value and the production operation value of the production equipment and the step-by-step calculation formula. S4, recording and storing the parameter adjustment value through the collaborative optimization module, determining the optimal parameter, and transmitting the value to the equipment through a network by utilizing a TCP/IP protocol.
Further, the data acquisition platform comprises a data acquisition module and a data uploading module.
Still further, the intelligent computing module comprises the steps of: first, through the formula
Calculating a parameter value of the first production facility, wherein V represents volume, unit: l, M represent mass, unit: kg, p stands for material density, unit: kg/L. In the second step, by the formula->
Calculating a first production run value and a second production run value, wherein the first production run value v represents flow rate in units of: l/s, V represents volume, unit: l, the second production run value t represents the filling time in units of: s. Thirdly, calculating the relation between the parameter value of the second production equipment and the parameter value of the third production equipment and the first production operation value through the formula v=alpha.C.P, wherein v represents the flow rate in units: l/s, α represents a correction coefficient, dimensionless, C represents the angle of the second generating device, unit: d, P stands for third production equipment constant, unit: l/(s.d); and calculating the angle C of the second production equipment through real-time data. Fourth step, by the formula ∈ ->
Calculating a third production run value, wherein the third year production run value P (s n ) Representing the qualification rate of the nth batch; fourth production run value s n Representing the number of filling barrels of the nth lot, and the fifth production run value S represents the number of filling barrels of all lots. />
Still further, according to the second step, the method is represented by the formula
And calculating the relation between the first production operation value and the second production operation value.
T is less than or equal to t at time 0 1 The flow rate corresponds to equation 4: v=a 1 t+b 1 I.e. the flow rate is increasing. At time t 1 ≤t＜t 2 The flow rate corresponds to equation 5: v=b 2 I.e. the flow rate is constant. At time t 2 ≤t≤t 3 The flow rate corresponds to equation 6: v= -a 3 t+b 3 I.e. the flow rate is continuously decreasing.
Calculating a through real-time data 1 、b 1 、b 2 、a 3 B 3 。a 1 Acceleration, b, representing the flow rate at the start of filling 1 A constant value representing the contemporaneous flow rate increase. b 2 Representing a constant flow rate in the middle of filling. a, a 3 Representing the rate of flow decrease in the later stages of filling, b 3 A constant value representing the decrease in flow rate at the end of filling.
By adopting the technical scheme, the invention has the beneficial effects that: the collaborative optimization system and the collaborative optimization method are distributed and deployed, cooperate with a plurality of devices, automatically collect and analyze field data, timely find out production efficiency bottlenecks, automatically remotely collaborative optimize related production parameters through a network by utilizing a TCP/IP protocol in the production process, improve the production energy and ensure safe and orderly efficient production.
Drawings
FIG. 1 is a schematic diagram of the system configuration of a remote production process collaborative optimization method according to the present invention.
Detailed Description
The invention further provides a remote production process collaborative optimization system and a remote production process collaborative optimization method, which are used for detecting the collaborative optimization of valve angle parameters of a typical chemical equipment filling machine by taking the attached drawings as an example.
Referring to fig. 1, a remote production process collaborative optimization method includes: the system comprises a data acquisition platform, a data storage module, an intelligent computing module and a collaborative optimization system formed by the collaborative optimization module, wherein the collaborative optimization system is communicated with related equipment 1, equipment 2 and equipment N of the filling machine through Ethernet and uses TCP/IP protocol.
The filling machine production line performs production according to preset procedures, production parameters, time plans and the like;
and normally starting the collaborative optimization system, and producing 200L steel barrels by a filling machine.
Firstly, the data acquisition platform comprises a data acquisition module and a data uploading module, and parameter values (refer to filling volume and valve angle) and production operation values (refer to time, flow rate and the like) of chemical production equipment are acquired and uploaded. In the continuous production process, the system collects various operation data of the filling machine through a data collection platform, such as: filling all barrels, filling cell barrels, filling weight, valve angle and filling time. For example: the first production equipment parameter filling volume of filling is 180L, the second production equipment parameter valve angle is 20d, the third production equipment parameter pipeline constant is 25L/(s.d), the first production operation numerical flow rate is 5L/s, the second production operation numerical filling time is 36s, the third production operation numerical qualification rate is 25%, the fourth production operation numerical n batches of barrels are 5000 barrels, and the fifth production operation numerical all batches of filling barrels are 20000 barrels.
The data storage module is used for storing the acquired parameter values and production operation values of the production equipment.
The intelligent computing module is used for acquiring an optimal parameter adjustment value of the production equipment through a step-by-step computing formula according to the data stored in the data storage module, the acquired parameter value and the production operation value of the production equipment;
firstly, calculating the filling volume of first production equipment, and obtaining the following formula 1:
wherein V represents volume, unit: l, M represent mass, unit: kg, p stands for material density, unit: kg/L; taking a 200L steel drum as an example, assuming that the filling material is polyethylene, the density of the material is 0.95Kg/L, the filling mass of each drum is 171Kg, and the filling volume is +.>
By formula 2:
calculating a first production run value and a second production run value, wherein the first production run value v represents flow rate in units of: l/s, V represents volume, unit: l, the second production run value t represents the filling time in units of: s; then->
According to the equipment type, the control strategy and the valve type.
By formula 3:
calculating the relation between the first production operation value and the second production operation value;
t is less than or equal to t at time 0 1 The flow rate corresponds to equation 4: v=a 1 t+b 1 I.e. the flow rate is increasing continuously;
at time t 1 ≤t＜t 2 The flow rate corresponds to equation 5: v=b 2 I.e. the flow rate is constant;
at time t 2 ≤t≤t 3 The flow rate corresponds to equation 6: v= -a 3 t+b 3 I.e., the flow rate is continually decreasing,
calculating a through real-time data 1 、b 1 、b 2 、a 3 B 3 ；a 1 Acceleration, b, representing the flow rate at the start of filling 1 A constant value representing the contemporaneous flow rate increase. b 2 Representing a constant flow rate in the middle of filling. a, a 3 Representing the rate of flow decrease in the later stages of filling, b 3 A constant value representing the decrease in flow rate at the end of filling.
By formula 7: v=α·c·p, and calculating the relationship between the parameter values of the second production plant valve, the parameter values of the third production plant pipe and the flow rate of the first production operation value, where v represents the flow rate in units of: l/s, alpha represents a correction coefficient, dimensionless, C represents the angle of the second generating device valve, in units of: d, P stands for third production equipment constant, unit: l/(s.d); and calculating the angle C of the second production equipment through real-time data. For example: correction coefficient α=0.01, tubing constant p=25l/(s·d), flow velocity v=5l/s, then
The optimal valve angle not only ensures high-efficiency production, but also ensures high-quality production, i.e. the filling qualification rate is higher than a certain value, by the formula 8:
calculating a third production run value, wherein the third year production run value P (s n ) Representing the qualification rate of the nth batch; fourth production run value s n Representing the number of filling barrels of the nth lot, and the fifth production run value S represents the number of filling barrels of all lots. And the formula 8 is the continuation of the algorithm, and the operation result of the formula 8 is used for judging the qualification rate of the product produced according to the operation of the current equipment parameters. If the other conditions are the same, the valve angle C is 20d, P (s n ) 97%, angle C30 d, P(s) n ) 98%, the valve is 30d relative to the optimum angle.
And finally, the collaborative optimization module records and stores the parameter adjustment value, determines the optimal parameter, and transmits the value to the equipment by utilizing a TCP/IP protocol through a network.
Further explaining the intelligent computing process:
1) Assuming that the real-time filling quality M, the material density p, the filling time t, t are known 1 、t 2 、t 3 Correcting the coefficient alpha and the pipeline constant P;
2) According to the formula 1, calculating the volume V according to the real-time filling mass M and the material density p;
3) According to formula 2, according to the filling time t, t 1 、t 2 、t 3 Calculate t 1 、t 2 、t 3 The flow velocity v of the time node;
4) According to formula 3, according to filling times t, t 1 、t 2 、t 3 Calculating a 1 、b 1 、b 2 、a 3 B 3 ；
5) According to formula 7, according to a 1 、b 1 、b 2 、a 3 B 3 Calculating a real-time flow rate and then calculating a valve angle C;
6) Gradually reducing the filling time t according to a certain proportion, and simultaneously reducing t in equal proportion 1 、t 2 、t 3 Producing a batch;
7) According to the formula 8, the qualification rate of the current batch is calculated, if the qualification rate is satisfied, the filling time is continuously reduced, and the t is reduced in equal proportion 1 、t 2 、t 3 Calculating an optimal valve angle;
8) If the product is not qualified, the filling time t is gradually increased, and the filling time t is reduced at the same time 1 、t 2 、t 3 And calculating the optimal valve angle.
Specific examples are as follows:
1) Assume that the real-time filling quality m=171 Kg, the material density p=0.95 Kg/L, the filling time t=36 s, t are known 1 ＝5s、t 2 ＝30s、t 3 =36 s, correction coefficient α=0.01, pipe constant p=25l/(s·d);
2) According to formula 1, calculating the volume v=180l according to the real-time filling mass M and the material density p;
3) According to formula 2, according to the filling time t, t 1 、t 2 、t 3 Calculate t 1 Flow velocity v=5l/s, t of time node 2 Flow velocity v=5l/s, t of time node 3 The flow velocity v=0l/s of the time node;
4) According to formula 3, according to filling times t, t 1 、t 2 、t 3 Calculating a 1 ＝1，b 1 ＝0，b 2 ＝5，
B 3 ＝-30；
5) According to formula 7, according to a 1 、b 1 、b 2 、a 3 B 3 Calculating the real-time flow velocity v=5l/s, and then calculating the valve angle
6) Gradually reducing the filling time t according to a certain proportion, and simultaneously reducing t in equal proportion 1 、t 2 T3, producing a batch;
7) According to the formula 8, the qualification rate of the current batch is calculated, if the qualification rate is satisfied, the filling time is continuously reduced, and simultaneously t1, t2 and t are reduced in equal proportion 3 Calculating an optimal valve angle;
8) If the product is not qualified, the filling time t is gradually increased, and the filling time t is reduced at the same time 1 、t 2 、t 3 And calculating the optimal valve angle.
The system automatically calculates the optimal valve angle of a plurality of devices according to the process, records and stores the value, and simultaneously, the value is transmitted to the devices by utilizing a TCP/IP protocol through a network, so that the devices execute high-efficiency and high-quality production.
The technical solution and the effects of the present invention are described in detail according to the embodiments shown in the drawings, but the above description is only the preferred embodiments of the present invention, and the scope of the present invention is not limited by the drawings, and all changes or modifications made according to the inventive concept are equivalent embodiments without departing from the spirit covered by the specification and the drawings.
Claims (3)
1. A remote production process collaborative optimization system is characterized in that: the collaborative optimization system comprises a data acquisition platform, a data storage module, an intelligent computing module and a collaborative optimization module; in the production process, the collaborative optimization system utilizes a TCP/IP protocol to automatically remotely and collaborative optimize related production parameters through a network, thereby improving the production energy and ensuring safe and orderly high-efficiency production;
the data acquisition platform is used for acquiring and uploading parameter values and production operation values of chemical production equipment;
the data storage module is used for storing the acquired parameter values and production operation values of the production equipment;
the intelligent computing module is used for acquiring the optimal parameter adjustment value of the production equipment through a step-by-step computing formula according to the data stored in the data storage module, the acquired parameter value and the production operation value of the production equipment;
the collaborative optimization module is used for recording and storing the parameter adjustment value, determining the optimal parameter, and transmitting the value to the equipment by utilizing a TCP/IP protocol through a network;
the intelligent computing module comprises the following steps of:
first, through the formula
Calculating a parameter value of the first production facility, wherein V represents volume, unit: l, M represent mass, unit: kg, p stands for material density, unit: kg/L;
second, through the formula
Calculating a first production run value and a second production run value, wherein the first production run value v represents flow rate in units of: l/s, V represents volumeUnits: l, the second production run value t represents the filling time in units of: s;
thirdly, calculating the relation between the parameter value of the second production equipment and the parameter value of the third production equipment and the first production operation value through the formula v=alpha.C.P, wherein v represents the flow rate in units: l/s, α represents a correction coefficient, dimensionless, C represents the angle of the second generating device, unit: d, P stands for third production equipment constant, unit: l/(s.d); calculating an angle C of the second production equipment through real-time data;
fourth, through the formula
Calculating a third production run value, wherein the third year production run value P (s n ) Representing the qualification rate of the nth batch; fourth production run value s n Representing the number of filling barrels of the nth batch, and the fifth production operation value S represents the number of filling barrels of all batches;
according to the second step, through the formula
Calculating the relation between the first production operation value and the second production operation value;
t is less than or equal to t at time 0 1 The flow rate corresponds to equation 4: v=a 1 t+b 1 I.e. the flow rate is increasing continuously;
at time t 1 ≤t＜t 2 The flow rate corresponds to equation 5: v=b 2 I.e. the flow rate is constant;
at time t 2 ≤t≤t 3 The flow rate corresponds to equation 6: v= -a 3 t+b 3 I.e. the flow rate is continuously decreasing;
calculating a through real-time data 1 、b 1 、b 2 、a 3 B 3 ；a 1 Acceleration, b, representing the flow rate at the start of filling 1 A constant value representing a contemporaneous increase in flow rate, b 2 Represents a constant flow rate in the middle of filling, a 3 Representing the rate of flow decrease in the later stages of filling, b 3 Representing the later stage of fillingConstant magnitude of flow rate reduction.
2. The remote production process collaborative optimization system according to claim 1, wherein: the data acquisition platform comprises a data acquisition module and a data uploading module.
3. The collaborative optimization method of a remote production process collaborative optimization system according to claim 2, wherein the method comprises a collaborative optimization system formed by a data acquisition platform, a data storage module, an intelligent computing module and a collaborative optimization module; the steps are as follows,
s1, acquiring and uploading parameter values and production operation values of chemical production equipment through the data acquisition platform;
s2, storing the acquired parameter values and production operation values of the production equipment through the data storage module;
s3, obtaining an optimal parameter adjustment value of the production equipment through a step-by-step calculation formula according to the data stored in the data storage module, the acquired parameter value and the production operation value of the production equipment through the intelligent calculation module;
s4, recording and storing the parameter adjustment value through the collaborative optimization module, determining the optimal parameter, and transmitting the value to the equipment through a network by utilizing a TCP/IP protocol.
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