CN107885084A - Absorbing natural gas tower sweetening process control method based on RBF and ADHDP - Google Patents
Absorbing natural gas tower sweetening process control method based on RBF and ADHDP Download PDFInfo
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- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 title claims abstract description 94
- 238000000034 method Methods 0.000 title claims abstract description 69
- 239000003345 natural gas Substances 0.000 title claims abstract description 47
- 238000004886 process control Methods 0.000 title claims abstract description 20
- 230000008569 process Effects 0.000 claims abstract description 40
- 238000013528 artificial neural network Methods 0.000 claims abstract description 16
- 238000006477 desulfuration reaction Methods 0.000 claims description 54
- 230000023556 desulfurization Effects 0.000 claims description 54
- 238000010521 absorption reaction Methods 0.000 claims description 42
- 238000012549 training Methods 0.000 claims description 23
- 238000011156 evaluation Methods 0.000 claims description 19
- 230000000694 effects Effects 0.000 claims description 10
- 239000007789 gas Substances 0.000 claims description 5
- 239000002253 acid Substances 0.000 claims description 4
- -1 alcohol amine Chemical class 0.000 claims description 4
- 238000010926 purge Methods 0.000 claims 1
- 230000004044 response Effects 0.000 abstract description 4
- 238000002474 experimental method Methods 0.000 abstract description 2
- 230000008878 coupling Effects 0.000 abstract 1
- 238000010168 coupling process Methods 0.000 abstract 1
- 238000005859 coupling reaction Methods 0.000 abstract 1
- 238000005457 optimization Methods 0.000 abstract 1
- 230000006870 function Effects 0.000 description 16
- 210000002569 neuron Anatomy 0.000 description 11
- 238000010586 diagram Methods 0.000 description 8
- 230000008901 benefit Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 230000004913 activation Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- CRVGTESFCCXCTH-UHFFFAOYSA-N methyl diethanolamine Chemical compound OCCN(C)CCO CRVGTESFCCXCTH-UHFFFAOYSA-N 0.000 description 2
- 238000000746 purification Methods 0.000 description 2
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 239000013064 chemical raw material Substances 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 230000001808 coupling effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 210000002364 input neuron Anatomy 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 239000011593 sulfur Substances 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10L—FUELS NOT OTHERWISE PROVIDED FOR; NATURAL GAS; SYNTHETIC NATURAL GAS OBTAINED BY PROCESSES NOT COVERED BY SUBCLASSES C10G, C10K; LIQUEFIED PETROLEUM GAS; ADDING MATERIALS TO FUELS OR FIRES TO REDUCE SMOKE OR UNDESIRABLE DEPOSITS OR TO FACILITATE SOOT REMOVAL; FIRELIGHTERS
- C10L3/00—Gaseous fuels; Natural gas; Synthetic natural gas obtained by processes not covered by subclass C10G, C10K; Liquefied petroleum gas
- C10L3/06—Natural gas; Synthetic natural gas obtained by processes not covered by C10G, C10K3/02 or C10K3/04
- C10L3/10—Working-up natural gas or synthetic natural gas
- C10L3/101—Removal of contaminants
- C10L3/102—Removal of contaminants of acid contaminants
- C10L3/103—Sulfur containing contaminants
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Abstract
The invention provides a kind of absorbing natural gas tower sweetening process control method based on RBF and ADHDP.Absorbing natural gas tower sweetening process is modeled using BP neural network and sweetening process is carried out as controlled device using the model and controls emulation experiment, optimization weights are constantly updated according to control error and performance index function, until obtaining optimum control signal, the optimum control of sweetening process is realized.The features such as absorbing natural gas tower sweetening process is complicated, and performance is uncertain, non-linear, strong coupling, dynamic, it is difficult to establish accurate mathematical modeling, control difficulty is larger.It is low for current absorbing natural gas tower sweetening process control method control accuracy, the problems such as time lag is big, unstable proposes a kind of sweetening process control method based on RBF and ADHDP, it not only ensure that the stability and control accuracy of control system, the response time is also reduced, is truly realized the real-time accurate control of absorbing natural gas tower sweetening process.
Description
Technical Field
The invention relates to a natural gas absorption tower desulfurization process control technology, in particular to a natural gas absorption tower desulfurization process control method based on combination of RBF and execution dependency heuristic dynamic programming (ADHDP).
Background
The natural gas is used as a high-quality and clean energy source and chemical raw material, is convenient to use and has higher comprehensive economic benefit. China has abundant natural gas resources, but about 30 percent of natural gas contains a large amount of sulfur elements, wherein H2The natural gas reserves with an S content of more than 1% account for 1/4 of the total reserves. H2The existence of S not only causes the corrosion of equipment and pipelines and harms human health, but also causes the pollution of the environment by combustion products. Thus, in the desulfurization of natural gas, H2S content control is particularly important.
The natural gas desulfurization absorption tower is an important component of a natural gas purification device and directly influences the natural gas purification effect. The natural gas raw material gas enters an absorption tower to be fully contacted with a Methyldiethanolamine (MDEA) solution in the tower for reaction, so that the aim of desulfurization is fulfilled, a physical and chemical reaction and a phase reaction are simultaneously performed in the whole process, the material conversion and energy transfer are involved, the characteristics of uncertainty, nonlinearity, strong coupling property, dynamic property and the like are greatly influenced by various uncertain factors, an accurate mathematical model is difficult to establish, and great difficulty is brought to the control of the desulfurization process of the absorption tower.
The existing control technology is mostly PID single loop control or simple cascade control, the automation degree of a control system is not high, and too much control parameters are adjusted by depending on expert experience, so that the control system has larger hysteresis, lower control precision, and difficult guarantee of the stability of the control system, and is difficult to achieve real-time accurate control.
Disclosure of Invention
The application provides a natural gas absorption tower desulfurization process control method based on RBF and ADHDP to solve the problems of low control precision, large time lag, unstable control system and the like existing in the prior absorption tower desulfurization process control technology, and guarantee the natural gas desulfurization effect.
In order to solve the technical problems, the application adopts the following technical scheme:
a natural gas absorption tower desulfurization process control method based on RBF and ADHDP is characterized by comprising the following steps:
step 1: by analyzing the desulfurization process of the natural gas absorption tower, determining that the main factors influencing the desulfurization effect of the natural gas are the acid natural gas treatment amount and the alcohol amine solution circulation amount which are respectively expressed by u1 and u2, and thus forming control variables u ═ u1 and u 2;
step 2: determining sample data output by the input sample data of the desulfurization process model, and establishing a natural gas absorption tower desulfurization process model by adopting a BP neural network;
and step 3: setting a control target valueUpdating evaluation network and execution network weight in the ADHDP method by using RBF neural network, and obtaining control signals u (k) (u 1, u 2) through the execution network and the evaluation network respectively]And a performance index function J (k) is used for establishing a natural gas absorption tower desulfurization process control method of RBF-ADHDP;
and 4, step 4: and (3) inputting the control signal u (k) ═ u1, u2 and the system state x (k) ═ x1, x2 at the current time as the desulfurization process model of the absorption tower, thereby obtaining a system output x (k + 1).
And 5: and (e) (k) calculating a control error E (k), if the control error is smaller than the expected error, finishing the training, otherwise, returning to the step 3.
As a further illustration, the step 3 is specifically performed according to the following steps:
step 3-1: updating the evaluation network and the execution network weight by using an RBF neural network according to the control error E (k);
step 3-2: calculating a control signal u (k);
step 3-3: the evaluation network output J (k +1) is calculated.
For further explanation, in step 5, the control error e (k) is calculated by the formula:
in the formula, the function u (k) is a utility function.
Compared with the prior art, the technical scheme that this application provided, the technological effect or advantage that have are: in the control of the desulfurization process of the natural gas absorption tower, the method has high control precision and high convergence rate, can improve the stability and control precision of a control system, reduce the response time of the control system and ensure the desulfurization effect of natural gas.
Drawings
FIG. 1 is a functional block diagram of the present invention;
FIG. 2 is a schematic diagram of a natural gas absorption tower desulfurization process model;
FIGS. 3-6 are schematic diagrams of model test results of a desulfurization process of a natural gas absorption tower;
FIG. 3H2A schematic diagram of S content prediction;
FIG. 4H2S content prediction relative error schematic diagram;
FIG. 5CO2Content prediction schematic diagram;
FIG. 6CO2Content prediction relative error schematic diagram;
FIG. 7 is a schematic view of the RBF-ADHDP control structure.
Detailed Description
The application provides an absorption tower desulfurization process control method based on RBF and ADHDP, and an invention principle block diagram is shown in figure 1. Referring to the prior art means, the technical scheme provided by the application has the following technical effects or advantages: the method adopts an intelligent algorithm for controlling the desulfurization process of the absorption tower, has higher control precision, can reduce the response time of a control system, can automatically adjust control parameters in real time, improves the stability of the control system, and really achieves the aim of real-time control.
In order to better understand the technical solutions, the technical solutions will be described in detail below with reference to fig. 2 to 7 of the specification and specific embodiments.
Firstly, entering the step 1: and selecting two parameters of the acid natural gas treatment amount and the circulation amount of the alcohol amine solution for absorbing the acid gas to form a control variable u ═ u1, u 2.
Step 2: using BP neural network to input1～inputnAnd x1～xnAnd training and checking the sample as an input and output sample so as to establish an absorption tower desulfurization process model. Wherein input is [ x1, x2, u1, u2]，x＝[x1,x2]N represents the number of samples, u1 and u2 represent the handling capacity of the raw natural gas and the circulation capacity of the alcohol amine solution in unit time respectively, and x1 and x2 represent H in the natural gas purified gas respectively2S content (mg/m)3) And CO2Content (%).
In this embodiment, an absorption tower desulfurization process model as shown in fig. 2 is established, the number of neurons in the input layer is 4, and the number of neurons in the output layer is 2; empirically, the hidden layer node is selected as(x is an input layer node, y is an output layer node, a is 1,2, 9), and an implicit layer node with the highest modeling test precision is selected to be 10 through sequential experiments; the hidden layer transfer function is a tansig function, and the output layer transfer function is a purelin function; the minimum value of the expected error is 0.0001, and the learning efficiency of the correction weight is 0.05. The modeling sample data is the actual production data of the plain gas field, the total number of the modeling sample data is 500 groups, 80% of the sample data is randomly selected to be used for model training, and the rest 20% of the sample data is used for model testing.
Setting the input of an absorption tower desulfurization process model as P, the number of input neurons as r, the number of neurons in a hidden layer as s1, a corresponding activation function as h1 and the output of the hidden layer as a 1; the number of neurons in the output layer is s2, the corresponding activation function is h2, the output is a2, and the target vector is T.
The step 2 of establishing the absorption tower desulfurization process model specifically comprises the following steps:
step 2-1: initializing, setting an initial value of iteration times g as 0, and simultaneously giving a random value in a (0,1) interval of W1, W2, B1 and B2;
step 2-2: random input sample Pj；
Step 2-3: for input sample PjForward computing the input and output of each layer of neuron of BP neural network;
the output of the ith neuron of the hidden layer is:
the output of the s-th neuron of the output layer is:
step 2-4: calculating an error e (g) based on the desired output T and the actual output a2 (g);
the error function is defined as:
step 2-5: judging whether the error E (g) meets the requirement, if not, entering the step 2-6, and if so, entering the step 2-8;
step 2-6: judging whether the iteration times g +1 are larger than the maximum iteration times, if so, entering the step 2-8, otherwise, entering the step 2-7;
step 2-7: and calculating the weight correction quantity delta W and correcting the weight.
① output layer weight change
For the weight from the ith input to the kth output, there are:
wherein, deltaki＝(tk-a2k)·h2′＝ek·h2′，ek＝tk-a2k。
② hidden layer weight change
For the weight from the jth input to the ith output, there are:
wherein,
the same can be obtained:
Δb1i＝η·δij
in the formula, η is learning efficiency, g is made to be g +1, and the step is shifted to step 3;
step 2-8: judging whether all training samples are finished, if so, finishing modeling, and otherwise, continuing to jump to the step 2-2;
through the above process, the prediction effect of the BP neural network can be obtained as shown in fig. 3 and 5, and the corresponding prediction error is shown in fig. 4 and 6. 3-6, the BP neural network training has higher precision in establishing the absorption tower desulfurization process model, can accurately predict the output of the system, and lays a foundation for the research of the natural gas desulfurization process control method.
And step 3: setting a desired control target valueApplying RBF nerve to update the weight of the evaluation network and the execution network in the ADHDP control method, and respectively obtaining control signals u (k) ([ u1, u 2) through the execution network and the evaluation network]And a performance index function J (k) to establish a natural gas absorption tower desulfurization process control method of RBF-ADHDP, wherein the control structure is shown in FIG. 7: the Action-RBF is an execution network, and the input and output are respectively a system state x (k) and a control signal u (k); ControlledObject is a model network, the input is a system state x (k) and a control signal u (k), and the output is a system next time state x (k + 1); Critic-RBF is an evaluation network, the input is x (k +1) and u (k +1), and the output is a performance index function J (k + 1); the evaluation network and the network training are respectively targeted to minimize the control error E (k) and the performance index function J (k), and the dotted line represents a network weight adjustment path.
In this embodiment, the control error calculation formula is:
in the formula, the function u (k) is a utility function.
In this embodiment, the training process for executing the network and evaluating the network is as follows:
(1) performing network training:
the execution network is designed by RBF neural network, and is set with Xa＝[xa1,xa2,…xam1]To implement the network input vector, m1To implement the number of network input variables, a ═ 1,2, … n1]，n1To perform the network training times.
Is n th1Weight vector between sub-training hidden layer M and output I, u (l) ═ ua1(l),ua2(l),…,uap(l)]Is n th1The secondary training performs the actual output of the network. Where l represents the number of iterations per training.
(2) Evaluation network training:
the evaluation network is also designed by the RBF neural network, and the training process is the same as that of the execution network. Is provided withTo evaluate the network input vector, m2Indicates the number of input variables of the evaluation network, c ═ 1,2, … n1]，n1To evaluate the number of network training times.
Is n th1The weight vector between the sub-training hidden layer M and the output I, J (l) is the nth1The secondary training evaluates the actual output of the network.
As a further illustration, the training processes of the execution network and the evaluation network are similar and performed simultaneously, and specifically include the following processes:
① initialization, set number of iterations n1Initial value of 0, and assigning WMI(0) Of a (0,1) interval
A random value;
② input Xa/Xc；
③ for input Xa/XcCalculating an input signal and an output signal of each layer of neuron of the RBF neural network in a forward direction;
④ calculating error E (k) according to the control error calculation formula;
⑤, judging whether the control error E (k) meets the control requirement, if not, entering ⑥, if yes, entering ⑨;
⑥ determining the number of iterations n1Whether +1 is greater than the maximum iteration number, if so, entering ⑨, otherwise, entering ⑦;
⑦ for input Xa/XcCalculating the local gradient delta of each layer of neurons in a reverse mode;
⑧ calculating weight correction quantity Δ W and correcting the weight, the calculation formula is Δ Wij＝η·δij·Aj，Wij(n1+1)＝Wij(n1)+ΔWij(n1) Wherein η is learning efficiency, let n1＝n1+1, jump to ③;
⑨ the training is complete.
(3) And (3) computing and executing network output:
the network hidden layer output is executed as:
wherein,b1 is X as the center of the execution of the network hidden layer neurons for the desired control objectiveatAndthe deviation therebetween.
The output of the execution network output layer is as follows: u (k) ═ Wa*AjIs what isEvaluating a control signal, wherein WaTo perform network weighting.
(4) Calculating and evaluating network output:
the evaluation network hidden layer output is:
wherein,representing the center of evaluating network hidden layer neurons, set according to training experience, with b2 being XcsAndthe deviation therebetween.
The evaluation network output layer output is:
J(k+1)＝Wc*Cj，
wherein, WcTo evaluate the network weight.
And 4, step 4: and (3) inputting the control signal u (k) ═ u1, u2 and the system state x (k) ═ x1, x2 at the current time as the desulfurization process model of the absorption tower, thereby obtaining a system output x (k + 1).
And 5: and (e) (k) calculating a control error E (k), if the control error is smaller than the expected error, finishing the training, otherwise, returning to the step 3.
Compared with the prior art, the technical scheme that this application provided, the technological effect or advantage that have are: in the process of desulfurization of the absorption tower, the method has high control precision and high convergence rate, can improve the stability and control precision of a control system, reduce the response time of the control system and ensure the desulfurization effect of natural gas.
The invention provides a natural gas absorption tower desulfurization process control method based on RBF and ADHDP. Firstly, training the actual production data of the absorption tower desulfurization by using a BP neural network, and establishing an absorption tower desulfurization process model, thereby bypassing the detailed problem in the desulfurization process mechanism, solving the problem of difficult modeling caused by the complicated desulfurization process, and laying a foundation for the research of the natural gas desulfurization process control method. And then, carrying out experimental research by taking the established model as a controlled object, controlling the desulfurization process of the absorption tower by adopting an ADHDP method, updating and optimizing an ADHDP evaluation network and an execution network weight by adopting an RBF neural network, and establishing the RBF-ADHDP-based desulfurization process control method of the absorption tower. The method gets rid of the excessive dependence on expert experience for a long time, solves the problems of low control precision, large time lag, unstable control system and the like in the existing absorption tower desulfurization process control technology, really achieves the purpose of real-time and accurate control of the absorption tower desulfurization process, also provides a new idea for solving the similar industrial control problem, and embodies the powerful functions of an artificial intelligence algorithm in the industry.
It should be noted that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the spirit and scope of the present invention.
Claims (4)
1. A natural gas absorption tower desulfurization process control method based on RBF and ADHDP is characterized by comprising the following steps:
step 1: by analyzing the desulfurization process of the natural gas absorption tower, determining that the main factors influencing the desulfurization effect are the acid natural gas treatment amount and the alcohol amine solution circulation amount which are respectively expressed by u1 and u2, and thus forming a control variable u ═ u1, u 2;
step 2: step 2: determining sample data output by the input sample data of the desulfurization process model, and establishing a natural gas absorption tower desulfurization process model by adopting a BP neural network;
and step 3: setting a desired control target valueUpdating evaluation network and execution network weight in the ADHDP method by using RBF neural network, and respectively obtaining control signals u (k) ═ u1 and u2 through the execution network and the evaluation network]And a performance index function J (k) is used for establishing a natural gas absorption tower desulfurization process control method of RBF-ADHDP;
and 4, step 4: inputting the control signal u (k) ═ u1, u2 and the current time system state x (k) ═ x1, x2 as the model of the desulfurization process of the absorption tower, thereby obtaining a system output x (k + 1);
and 5: and (e) (k) calculating a control error E (k), if the control error is smaller than the expected error, finishing the training, otherwise, returning to the step 3.
2. The method for controlling the desulfurization process of a natural gas absorption tower based on RBF and ADHDP in accordance with claim 1, wherein:
when the absorption tower desulfurization process model is established in the step 2, input is [ x1, x2, u1 and u2]]Sample data was input as a model, while x ═ x1, x2]As sample data of model output, x1 and x2 represent H in the natural gas purge gas, respectively2S content (mg/m)3) And CO2Content (%).
3. A method for controlling a desulfurization process of a natural gas absorption tower based on RBF and ADHDP according to claim 1, wherein the method for controlling RBF-ADHDP in the step 3 comprises the steps of:
step 3-1: updating the evaluation network and the execution network weight by using an RBF neural network according to the control error E (k);
step 3-2: calculating a control signal u (k);
step 3-3: the evaluation network output J (k +1) is calculated.
4. The method for controlling the desulfurization process of a natural gas absorption tower based on RBF and ADHDP in claim 1, wherein the control error E (k) in the step 5 is calculated by the following formula:
<mrow> <mo>|</mo> <mo>|</mo> <mi>E</mi> <mo>|</mo> <mo>|</mo> <mo>=</mo> <munder> <mo>&Sigma;</mo> <mi>k</mi> </munder> <mi>E</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <munder> <mo>&Sigma;</mo> <mi>k</mi> </munder> <msup> <mrow> <mo>&lsqb;</mo> <mover> <mi>J</mi> <mo>^</mo> </mover> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>U</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>&gamma;</mi> <mover> <mi>J</mi> <mo>^</mo> </mover> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mn>2</mn> </msup> </mrow>
in the formula, the function u (k) is a utility function.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009058229A1 (en) * | 2007-10-30 | 2009-05-07 | Saudi Arabian Oil Company | Desulfurization of whole crude oil by solvent extraction and hydrotreating |
| WO2013023216A1 (en) * | 2011-08-11 | 2013-02-14 | Arizona Board Of Regents On Behalf Of The University Of Arizona | High sulfur content copolymers and composite materials and electrochemical cells and optical elements using them |
| CN104636600A (en) * | 2014-12-31 | 2015-05-20 | 中国石油化工股份有限公司中原油田普光分公司 | High sulfur natural gas purifying process modeling and optimizing method based on extreme learning machine |
| CN104656441A (en) * | 2014-12-29 | 2015-05-27 | 重庆科技学院 | Natural gas purification process modeling optimization method based on unscented kalman neural network |
| CN104696080A (en) * | 2014-10-31 | 2015-06-10 | 重庆邮电大学 | Observer-based intelligent dual-integral sliding-mode control method for electronic throttle valve |
| CN105139078A (en) * | 2004-10-20 | 2015-12-09 | 艾默生过程管理电力和水力解决方案有限公司 | Method and apparatus for providing load dispatch and pollution control optimization |
| CN106777866A (en) * | 2016-11-14 | 2017-05-31 | 重庆科技学院 | Technology Modeling and optimization method are purified towards energy-saving high sulfur-containing natural gas |
| CN106777465A (en) * | 2016-11-14 | 2017-05-31 | 重庆科技学院 | High sulfur-containing natural gas purify technique dynamic evolutionary modeling and energy conservation optimizing method |
-
2017
- 2017-11-13 CN CN201711117446.2A patent/CN107885084B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105139078A (en) * | 2004-10-20 | 2015-12-09 | 艾默生过程管理电力和水力解决方案有限公司 | Method and apparatus for providing load dispatch and pollution control optimization |
| WO2009058229A1 (en) * | 2007-10-30 | 2009-05-07 | Saudi Arabian Oil Company | Desulfurization of whole crude oil by solvent extraction and hydrotreating |
| WO2013023216A1 (en) * | 2011-08-11 | 2013-02-14 | Arizona Board Of Regents On Behalf Of The University Of Arizona | High sulfur content copolymers and composite materials and electrochemical cells and optical elements using them |
| CN104696080A (en) * | 2014-10-31 | 2015-06-10 | 重庆邮电大学 | Observer-based intelligent dual-integral sliding-mode control method for electronic throttle valve |
| CN104656441A (en) * | 2014-12-29 | 2015-05-27 | 重庆科技学院 | Natural gas purification process modeling optimization method based on unscented kalman neural network |
| CN104636600A (en) * | 2014-12-31 | 2015-05-20 | 中国石油化工股份有限公司中原油田普光分公司 | High sulfur natural gas purifying process modeling and optimizing method based on extreme learning machine |
| CN106777866A (en) * | 2016-11-14 | 2017-05-31 | 重庆科技学院 | Technology Modeling and optimization method are purified towards energy-saving high sulfur-containing natural gas |
| CN106777465A (en) * | 2016-11-14 | 2017-05-31 | 重庆科技学院 | High sulfur-containing natural gas purify technique dynamic evolutionary modeling and energy conservation optimizing method |
Non-Patent Citations (4)
| Title |
|---|
| LIMINMA，等: "Energy Consumption Optimization of High Sulfur Natural Gas Purification Plant Based on Back Propagation Neural Network and Genetic", 《ENERGY PROCEDIA》 * |
| 罗艳红: "基于神经网络的非线性系统自适应优化控制研究", 《中国博士学位论文全文数据库信息科技辑》 * |
| 辜小花，等: "基于大数据的高含硫天然气脱硫工艺优化", 《天然气工业》 * |
| 邓骥: "某天然气脱硫装置适应性分析与动态特性研究", 《中国优秀硕士学位论文全文数据库工程科技I辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110209130A (en) * | 2019-05-07 | 2019-09-06 | 重庆科技学院 | A kind of supply of cooling, heating and electrical powers type micro-grid system optimizing operation method based on ADHDP |
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