CN101859103B - On-line calculation and self-adaptation nonlinear prediction control method of catalytic cracking reaction depth - Google Patents

Abstract

The invention relates to an on-line calculation and self-adaptation nonlinear prediction control method of a catalytic cracking reaction depth, belonging to the technical field of automatic control of industrial processes. The method is characterized by comprising the following steps of: correcting the parameter of a regeneration valve flow characteristic model on line by utilizing a relatively accurate catalyst circulating amount obtained by calculating the heat balance of a regenerator; then finishing the real-time calculation of the catalyst circulating amount based on the corrected regeneration valve flow characteristic model; and on that basis, calculating the catalytic cracking reaction heat on line based on a riser dynamic mathematical model and realizing the self-adaptation nonlinear prediction control of the riser reaction depth.

Description

Catalytic cracking reaction depth is at line computation and self-adaptation nonlinear prediction control method

Technical field

The present invention relates to catalytic cracking reaction depth at line computation and self-adaptation nonlinear prediction control method, belong to soft measurement of petrochemical complex catalytic cracking production run and automation field.

Background technology

Catalytic cracking unit is one of most important mink cell focus reforming unit in the petroleum refinery.Feedstock oil makes mink cell focus generation cracking reaction under the effect of heat and catalyzer, change high-value products such as rich gas, gasoline and diesel oil into.Say that from economic benefit benefit over half in the oil refining enterprise obtains by catalytic cracking.Therefore the even running of the optimal control of application of advanced technology implement device, and for on-line optimization provides condition, have important practical significance undoubtedly and tangible economic benefit.

Mostly existing catalytic cracking optimization control scheme is that with temperature of reaction (riser outlet temperature) be main, but in fact catalytic cracking is a course of reaction, and to device operation and product distribution influence maximum is reaction depth.Temperature of reaction just influences a factor of reaction depth, in actual production process, also has the factor that much influences reaction depth.For addressing this problem, Yuan Pu etc. (observation of catalytic cracking reaction process and control, Chinese patent: ZL 90108193.0) propose the notion of macroreaction heat (per kilogram charging when cracking reaction required heat kJ/kg).Macroreaction heat combines the various factors that influences reaction depth, and it is steady to keep reaction heat, can make reaction depth steady, thereby makes whole device operation more steady.Jiang Qingyin (2003) is equally based on the thought of reaction depth control, and proposing a kind of is that the closed loop of main regulation and control amount is transferred excellent control system with conversion rate of products.

Realization is exactly the online in real time calculating of macroreaction heat based on a key of the catalytic cracking reaction depth optimal control of macroreaction heat.The factor that influences reaction depth is a lot, and one of them important factor is the regenerated catalyst internal circulating load.Because the danger and the complicacy of production run, the catalyst recirculation amount can not directly be measured with measurement instrument.The method of present industrial calculating catalyst circulation amount mainly is divided three classes.1, utilize regenerator steady-state heat balance relation to calculate the catalyst recirculation amount, as Lin Shixiong etc. (petroleum refining engineering (third edition) [M], 2007,374-377); 2, utilize the material balance of carbon in the regenerator to calculate the catalytic cycle amount, like (comparison of FCC catalyst recirculation amount computing method, oil refining design, 1990,4 (1): 41-43) such as Wei Fei; 3, utilize catalyst stream dynamic characteristic and regenerating valve property calculation catalytic cycle amount, like (observation of catalytic cracking reaction process and control, Chinese patent: ZL 90108193.0) such as Yuan Pu.

But existing computing method face following problem:

1, adopt regenerator heat Balance Calculation catalyst recirculation measurer that higher precision is arranged, but because the oxygen content of smoke gas measurement has the regular hour hysteresis; Reclaimable catalyst flow to regenerator by riser through stripping section, and its temperature and burnt content all have 1-2 minute time lag to regenerator, and the real-time of its calculating can not satisfy the requirement of riser being carried out advanced control.

2, reclaimable catalyst is decided carbon, regenerated catalyst is decided carbon and can only be obtained through the off-line chemical examination, has sizable time lag, therefore, utilizes carbon balance method to calculate the real-time requirement that the catalyst recirculation amount can not satisfy production monitoring and control.

3, utilize regenerating valve discharge characteristic Model Calculation catalyst recirculation amount speed fast, but because the catalyst recirculation amount is the nonlinear function about the guiding valve aperture, and relevant with the fluidized state and the density of catalyzer.When the regenerating valve aperture changes greatly or catalyst fluidization state when changing, if adopt the regenerating valve characteristic model of fixing to calculate, its accuracy will be difficult to be guaranteed.

Therefore; Design and a kind ofly calculate relatively accurate and reflect the catalyst recirculation amount online in real time computing method of actual change fast, the realization response degree of depth is in line computation, and realizes the control of catalytic cracking unit reaction depth on this basis; The holdout device smooth long term running is very necessary.

Summary of the invention

The present invention seeks to provides a kind of reaction depth at line computation and self-adaptation nonlinear prediction control method to catalytic cracking unit.

For achieving the above object; The mode that the present invention adopts regenerator heat Balance Calculation model and regenerating valve discharge characteristic model to combine; Utilize relatively accurate regenerator heat Balance Calculation on-line correction regenerating valve model coefficient as a result, and realize real-time monitored the catalyst recirculation amount by the regenerating valve model after proofreading and correct.At last, accomplish the online in real time calculating of catalytic cracking reaction heat according to the riser dynamic mathematical models.In addition, consider the characteristics of regenerating valve discharge characteristic model, compare with the dynamic perfromance of riser reactive moieties that is: that the dynamic perfromance of regenerating valve can be ignored, and steady-state characteristic can change with working conditions change.Based on these characteristics; In controlling models; The regenerating valve steady-state model is separated with riser reactive moieties nonlinear dynamical model; On the basis of realizing the control of riser reactive moieties nonlinear prediction,, realize the self-adaptation nonlinear PREDICTIVE CONTROL of regenerating valve aperture to reaction depth according to the regenerating valve Model Calculation catalyst recirculation amount of on-line correction.

The present invention at first utilizes regenerating valve discharge characteristic Model Calculation catalyst recirculation amount; Whether reaction-the regenerating section of judgment means reaches balance then; If device balance; The catalyst recirculation amount online updating regenerating valve model coefficient that then calculates with the regenerator thermal balance model goes out the catalyst recirculation amount with the regenerating valve Model Calculation after upgrading then, is used for calculating lifting reaction heat and design self-adaptation nonlinear fallout predictor.

Catalytic cracking reaction depth is characterized in that at line computation and self-adaptation nonlinear prediction control method, on control computer, realizes according to following steps:

Step (1) control computer initialization

Be provided with: first catalyst recirculation amount computing module, second catalyst recirculation amount computing module, regenerating valve model coefficient correction module, riser reaction heat computing module and riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module;

Step (2) is provided with the SI T of control computer _s, sampling period K * Ts, sampling instant k=1; 2; K, said control computer utilizes real-time data base to obtain field data from dcs DCS online, comprises at least: regenerating valve aperture measurement value ivp (k); Regenerating valve drop measurement value Δ P (k), the catalyst temperature measured value T of entering regenerator ₁(k), flow out the catalyst temperature measured value T of regenerator ₂(k), the temperature in measured value T of first section riser _R0(k), the outlet temperature measured value T of first section riser _R1(k), the outlet temperature measured value T of second section riser _R2(k), feedstock oil charging mass flow measurement G _Oil(k), feedstock oil feeding preheating measured temperature T _Oil(k), promote steam mass flow measured value G _w(k), advance riser and promote vapor (steam) temperature measured value T _w(k), advance regenerator volume of air flow measurements F _a(k), oxygen content of smoke gas measured value O _Fg(k), regenerated flue gas measured temperature T _Fg(k), get into the air themperature measured value T of regenerator _a(k), regeneration heat collector cooling water flow measured value F _Ex(k), the regeneration heat collector cooling water inlet measured temperature T of place _{Ex_in}(k), the measured temperature T of regenerator heat collector coolant outlet place _{Ex_out}And the total reaction heat setting value of given two-stage riser (k),

With second section riser outlet temperature setting value

Step (a 3) k sampling instant, said first catalyst recirculation amount computing module are pressed regenerating valve discharge characteristic Model Calculation catalyst recirculation amount G according to ivp (k) and Δ P (k) that step (2) obtains _C(k):

$G_C\left(k\right)=\mathrm{Cv}\left(\mathrm{Ivp}\left(k\right)\right)\×\sqrt{\mathrm{\Δ\; P}\left(k\right)},$ Wherein:

G _C(k): k sampling instant, by the catalyst recirculation amount that regenerating valve discharge characteristic Model Calculation obtains, kg/h, Cv (ivp (k)): about the unknown function of regenerating valve aperture ivp, m * h, adopt polynomial function to be similar to:

$\mathrm{Cv}\left(\mathrm{Ivp}\left(k\right)\right)=\underset{n=0}{\overset{N}{\mathrm{\Σ}}}{a}_n(k-1)\×{\mathrm{Inp}}^n\left(k\right),$ Wherein:

N=2 is the polynomial function order, a _n(k-1) be the multinomial coefficient of k-1 sampling instant, its initial value is a ₀(0)=-15.915, a ₁(0)=1.159, a ₂(0)=0.058;

The said regenerating valve model coefficient of step (4) correction module is pressed the described multinomial coefficient of following step step of updating (3) successively,

Step (a 4.1) k sampling instant, the T that obtains according to step (2) _R2(k) and T ₂(k), judge whether catalytic cracking reaction-regenerating section reaches stable state, if satisfy following condition:

$|X\left(k\right)-\underset{k_n=1}{\overset{k_N}{\mathrm{\Σ}}}\frac{X(k-k_n)}{k_N}|\≤\mathrm{\ϵ},$

Think that then reaction-regenerating section reaches stable state, wherein: vectorial X (k-k _n), k _n=1,2 ..., k _NBe by k-k _nThe T of individual sampling instant _R2(k-k _n) and T ₂(k-k _n) bivector formed, data length k _N=10, ε=[1,4] ' be threshold value,

Step (4.2) changes said step (4.3) over to if catalytic cracking reaction-regenerating section reaches stable state, utilizes regenerator thermal equilibrium formula to calculate the catalyst recirculation amount and upgrades said regenerating valve model coefficient, if do not reach stable state, then makes a _n(k)=a _n(k-1), n=0 ..., N changes step (5) over to and calculates,

Said second the catalyst recirculation amount computing module of step (4.3) is according to the T that obtains in the step (2) ₁(k) and T ₂(k), press regenerator thermal equilibrium formula and calculate the catalyst recirculation amount

${\hat{G}}_C\left(k\right)=\frac{Q_C\left(k\right)}{{\mathrm{Cp}}_{\mathrm{Cat}}\×(T_2\left(k\right)-T_1\left(k\right))},$ Wherein:

K sampling instant is by the catalyst recirculation amount that regenerator thermal equilibrium formula calculates, kg/h, Cp _Cat: catalyzer specific heat, kJ/ (kg. ℃), given value,

Q _C(k)=Q _O(k)-Q _a(k)-Q _T(k)-Q _q(k): k sampling instant, catalyzer is taken away heat, kJ/h,

K sampling instant, the unit interval is always burnt thermal discharge, kJ/h, Δ H _Cb: the heat that burning 1kg carbon produces, kJ/ (kg carbon), given value, v _Oc: burning 1kg carbon oxygen utilization, m ³/ (kg carbon), given value,

Q _a(k)=F _a(k) ρ _AirCp _Air(T _Fg(k)-T _a(k)): k sampling instant, air is taken away heat, kJ/h, ρ _Air: atmospheric density, kg/m ³, given value, Cp _Air: air specific heat, kJ/ (kg. ℃), given value,

Q _T(k)=0.063 * Q _O(k): k sampling instant, desorption heat, kJ/h, Q _q(k)=F _Ex(k) Cp _Water(T _{Ex_out}(k)-T _{Ex_in}(k)): k sampling instant, regeneration heat collector heat-obtaining amount, kJ/h, Cp _Water: chilled water specific heat, kJ/ (kg. ℃), given value,

One group of data that step (4.4) makes

obtains being used to upgrade multinomial coefficient are utilized the said multinomial coefficient of RLS step of updating (3) then, and it is following to upgrade iterative formula:

wherein:

K (k) is the coefficient correction gain matrix of k sampling instant, and P (k) is a k covariance matrix constantly, its initial value P (0)=10 ⁵* I, I are unit matrix,

λ (k)=0.98 is a forgetting factor, θ (k)=[a ₀(k), a ₁(k) ..., a _N(k)] ' and be the multinomial coefficient of k sampling instant, subscript symbol " ' " representing matrix transposition, down together,

Said first catalyst recirculation amount computing module of step (4.5) is according to the multinomial coefficient a that upgrades _n(k) recomputate the catalyst recirculation amount

And send said dcs DCS to show and the subsequent calculations use;

Step (5) riser reaction heat computing module is divided at said riser under two sections the condition, i=1, and 2, calculate the reaction heat of each section riser,

Step (5.1) is set the model of a said riser:

$M{p}_i\frac{{\mathrm{DT}}_{\mathrm{Ri}}}{\mathrm{Dt}}+(G_{\mathrm{Oil}}{\mathrm{Cp}}_{\mathrm{Oil}}+G_C{\mathrm{Cp}}_{\mathrm{Cat}}+G_w{\mathrm{Cp}}_w)(T_{\mathrm{Ri}}-T_{r(i-1)})-H_{\mathrm{Lossi}}=G_{\mathrm{Oil}}{H}_{\mathrm{Ri}},$ Wherein:

T _Ri: the outlet temperature of i section riser, ℃,

T _R0=T _{R (i-1)}, i=1: the temperature in of the 1st section riser, ℃,

Mp _i: the thermal capacity of i section riser material and wall, kJ/ ℃, given value,

Cp _Oil: oil gas specific heat, kJ/ (kg. ℃), given value,

Cp _Cat: catalyzer specific heat, kJ/ (kg. ℃), given value,

Cp _w: water vapour thermal capacity, kJ/kg, given value,

C _C: by the catalyst recirculation amount that the regenerating valve Model Calculation obtains, kg/h,

H _Lossi: the thermal loss of i section riser, kJ/h, given value,

H _Ri: the reaction heat of i section riser, kJ/kg feedstock oil,

Step (5.2) discretize riser model equation obtains the reaction heat that the reaction heat computing formula is calculated said each section riser:

$H_{\mathrm{Ri}}\left(k\right)=(1-\mathrm{\γ})H_{\mathrm{Ri}}(k-1)+\mathrm{\γ}({\mathrm{Mp}}_i\frac{T_i\left(k\right)-T_i(k-1)}{T_s}+H_{\mathrm{Oi}}\left(k\right)-H_{\mathrm{Lossi}}\left(k\right))/G_{\mathrm{Oil}},$ Wherein:

H _Oi(k)=(G _Oil(k) Cp _Oil+ G _C(k) Cp _Cat+ G _w(k) Cp _w) (T _Ri(k)-T _{R (i-1)}(k)), 0＜γ≤1st, filtering parameter, the total reaction heat that finally obtains two-stage riser does

Step (6) is k sampling instant, and said riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module is carried out reaction depth control by following step,

Step (6.1) is set up the riser nonlinear dynamic mathematical model towards dynamic control:

$\left\{\begin{array}{c}\frac{{\mathrm{<figure-callout\; id="1"\; label="DT\; r"\; filenames="HSA00000159643200021.png"\; state="\{\{state\}\}">DT</figure-callout>}}_r}{\mathrm{Dt}}=-\frac{1}{s_{T1}(1+{\mathrm{\&Gamma;}}_1)}(T_{r1}-T_{r0})-\frac{{\mathrm{\&Lambda;}}_1}{s_{T1}(1+{\mathrm{\&Gamma;}}_1)}\frac{G_C}{G_{\mathrm{<figure-callout\; id="1"\; label="Oil\; C\; c"\; filenames="HSA00000159643200021.png"\; state="\{\{state\}\}">Oil</figure-callout>}}}{C}_c{1}_{}\\ \frac{{\mathrm{DT}}_{r2}}{\mathrm{Dt}}=-\frac{1}{s_{T2}(1+{\mathrm{\&Gamma;}}_2)}(T_{r2}-T_{r1})-\frac{{\mathrm{\&Lambda;}}_2}{s_{T2}(1+{\mathrm{\&Gamma;}}_2)}\frac{G_C}{G_{\mathrm{Oil}}}(C_{c2}-C_{c1})\end{array}\right.,$ Wherein:

oil gas is in the hold-up time of i section riser; H

V _Ri: i section riser volume, m ³, given value,

ρ _Oil: the hydrocarbon density of the 1st section riser porch, kg/m ³, given value,

${\mathrm{\&Gamma;}}_i=\frac{\frac{V_{\mathrm{Li}}{\mathrm{\&rho;}}_L}{V_{\mathrm{Ri}}{\mathrm{\&rho;}}_{\mathrm{Oil}}}{\mathrm{Cp}}_m}{{\mathrm{Cp}}_{\mathrm{Oil}}+\frac{G_C}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_{\mathrm{Cat}}+\frac{G_w}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_w}:$ Dimensionless thermal capacitance correction coefficient,

V _Li: i section riser antiwear heat resisting layer volume, m ³, given value,

ρ _L: riser antiwear heat resisting layer density, kg/m ³, given value,

Cp _m: the specific heat of riser antiwear heat resisting layer, kJ/ (kg. ℃), given value,

G _C: by the catalyst recirculation amount that the regenerating valve Model Calculation obtains, kg/h,

${\mathrm{\&Lambda;}}_i=\frac{\mathrm{\&Delta;}{H}_{\mathrm{CR}}}{{\mathrm{Cp}}_{\mathrm{Oil}}+\frac{G_C}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_{\mathrm{Cat}}+\frac{C_w}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_w}:$ The ratio of reaction heat and material specific heat, ℃,

Δ H _CR: reaction heat, the kJ/kg catalyzed carbon, given value,

C _Ci: the catalyzed carbon content of i section riser exit catalyzer, %, given value

Cp _{Oil_L}: feedstock oil specific heat, kJ/ (kg. ℃), given value,

Δ H _v: the feedstock oil heat of gasification, kJ/kg feedstock oil, given value,

The output variable of said riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL is second section riser outlet temperature T _R2Total reaction heat H with two-stage riser _r, control variable is the fuel oil preheating temperature T _OilWith regenerating valve aperture ivp; Based on above-mentioned riser nonlinear dynamic mathematical model, the purpose of said reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module is control fuel oil preheating temperature T _OilWith regenerating valve aperture ivp, make second section riser outlet temperature T _R2With reaction heat H _rReach setting value separately

With

Step (6.2) is k sampling instant; Riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module is through control fuel oil preheating temperature and regenerating valve aperture; Setting value

and

promptly adopt the Levenberg-Marquardt optimized Algorithm to find the solution following optimization problem to make second section riser outlet temperature and reaction heat reach separately:

$\underset{j=1,...L_2}{\underset{T_{\mathrm{oil}}(k+l_2),\mathrm{ivp}(k+l_2)}{\min }}J=\underset{{\mathrm{<figure-callout\; id="1"\; label="l"\; filenames="HSA00000159643200021.png"\; state="\{\{state\}\}">l</figure-callout>}}_1=1}{\overset{L_1}{\mathrm{\&Sigma;}}}{\left[\begin{array}{c}{H}_r(k+l_1)-H_r^{*}\\ T_{r2}(k+l_1)-T_{r2}^{*}\end{array}\right]}^{\&prime;}Q\left[\begin{array}{c}{H}_r(k+l_1)-H_r^{*}\\ T_{r2}(k+l_1)-T_{r2}^{*}\end{array}\right]$

$+\underset{l_2=1}{\overset{L_2}{\mathrm{\&Sigma;}}}{\left[\begin{array}{c}{T}_{\mathrm{oil}}(k+l_2)-T_{\mathrm{oil}}(k+l_2-1)\\ \mathrm{ivp}(k+l_2)-\mathrm{ivp}(k+l_2-1)\end{array}\right]}^{\&prime;}R\left[\begin{array}{c}{T}_{\mathrm{oil}}(k+l_2)-T_{\mathrm{oil}}(k+l_2-1)\\ \mathrm{ivp}(k+l_2)-\mathrm{ivp}(k+l_2-1)\end{array}\right]$

Controlled amount T _Oil(k+l ₂), ivp (k+l ₂), l ₂=1 ..., L ₂, wherein: L ₁=10 is prediction step, L ₂=2 are control step-length, H _r(k+l ₁) be according to (6.1) said riser model prediction k+l ₁Reaction calorific value constantly, T _R2(k+l ₁) be according to (6.1) said riser model prediction k+l ₁The 2nd section riser outlet temperature value constantly, T _Oil(k+l ₂) be k+l ₂The predicted value of fuel oil preheating temperature constantly, ivp (k+l ₂) be k+l ₂The predicted value of regenerating valve aperture constantly, Be the output variable weighting matrix,

It is the control variable weighting matrix;

The T that the said riser reaction depth of step (7) self-adaptation nonlinear PREDICTIVE CONTROL module obtains step (6.2) _Oil(k+1) and ivp (k+1) respectively as k+1 setting value and the k+1 setting value of regenerating valve aperture constantly of fuel oil preheating temperature PID controller constantly, change k+1 then over to constantly, repeated execution of steps (2)-(7) are until the total K of the sampling that reaches setting.

Above-mentioned technical method has following effect: utilize and calculate catalyst recirculation amount on-line correction regenerating valve model coefficient relatively accurate but that have the regenerator heat Balance Calculation of big time lag; Obtain the regenerating valve model that can adaptation condition changes thus, satisfied relative precision and real-time double requirements that the catalyst recirculation amount is estimated; On this basis, based on the riser dynamic model accomplish reaction heat in line computation, make it satisfy the requirement of implementing advanced control; When the riser mathematical model of setting up towards dynamic control, according to the characteristics of regenerating valve model, that is: to compare with the dynamic perfromance of riser reactive moieties, the dynamic perfromance of regenerating valve can be ignored, and steady-state characteristic can change with the variation of operating mode.The regenerating valve steady-state model is separated with riser reactive moieties nonlinear dynamical model; On the basis of realizing the control of riser reactive moieties nonlinear prediction; According to the regenerating valve Model Calculation of on-line correction catalyst recirculation amount again; Realize the self-adaptation nonlinear PREDICTIVE CONTROL of regenerating valve aperture, for the safety in production and the optimal control of catalytic cracking unit are offered help to reaction depth.

Description of drawings

Fig. 1 is that the catalytic cracking reaction depth of the embodiment of the invention is at line computation and adaptive prediction control method process flow diagram;

Fig. 2 is the on-the-spot operating structure synoptic diagram of the embodiment of the invention;

Fig. 3 is the control computer calculation flow chart of the embodiment of the invention.

Embodiment

Below in conjunction with accompanying drawing and embodiment, specific embodiments of the invention describes in further detail.Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.

The invention is characterized in: utilize relatively accurate but heat Balance Calculation with the big time lag parameter of on-line correction regenerating valve model as a result; Obtain the self-adaptation regenerating valve model that can adaptation condition changes thus, satisfied relative precision and real-time double requirements that the catalyst recirculation amount is estimated.At last, based on the riser dynamic mathematical models accomplish reaction heat in line computation; When the riser mathematical model of setting up towards dynamic control, according to the characteristics of regenerating valve model, that is: to compare with the dynamic perfromance of riser reactive moieties, the dynamic perfromance of regenerating valve can be ignored, and steady-state characteristic can change with the variation of operating mode.The regenerating valve steady-state model is separated with riser reactive moieties nonlinear dynamical model; On the basis of realizing the control of riser reactive moieties nonlinear prediction; Extract the steady state relation of the nonlinear time-varying between regenerating valve aperture and catalyst recirculation amount, the self-adaptation nonlinear PREDICTIVE CONTROL of the final realization response degree of depth.Its overall implementation process flow diagram and the overall schematic diagram of realizing comprise following steps referring to accompanying drawing 1-3:

1, step 1: the control computer initialization, the gatherer process data are also carried out pre-service to data

This step mainly comprises following unit module:

Be provided with: first catalyst recirculation amount computing module, second catalyst recirculation amount computing module, regenerating valve model coefficient correction module, riser reaction heat computing module and riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module;

Process actual measurement variable and chemical examination variable data collecting unit: host computer utilizes real-time data base to obtain field data from dcs DCS online; Process variable comprises: regenerating valve aperture measurement value ivp (k), regenerating valve drop measurement value Δ P (k), the catalyst temperature measured value T of entering regenerator ₁(k), flow out the catalyst temperature measured value T of regenerator ₂(k), the temperature in measured value T of first section riser _R0(k), the outlet temperature measured value T of first section riser _R1(k), the outlet temperature measured value T of second section riser _R2(k), feedstock oil charging mass flow measurement G _Oil(k), feedstock oil feeding preheating measured temperature T _Oil(k), promote steam mass flow measured value G _w(k), advance riser and promote vapor (steam) temperature measured value T _w(k), advance regenerator volume of air flow measurements F _a(k), oxygen content of smoke gas measured value O _Fg(k), regenerated flue gas measured temperature T _Fg(k), get into the air themperature measured value T of regenerator _a(k), regeneration heat collector cooling water flow measured value F _Ex(k), the regeneration heat collector cooling water inlet measured temperature T of place _{Ex_in}(k), the measured temperature T of regenerator heat collector coolant outlet place _{Ex_out}And the total reaction heat setting value of given two-stage riser (k), With second section riser outlet temperature setting value

Variable data processing unit: actual measurement process variable and chemical examination variable are carried out Filtering Processing to reduce The noise; The range and the dimension of unified variable are adjusted zero point, and whether detection variable transfinites;

Pure retardation time the unit: by the size of device relevant device and fluid-transporting tubing, interactional time lag between definite in real time each variable; And utilize the historical trend curve of surveying correlated variables, check these pure retardation time;

2, step 2: set up regenerating valve discharge characteristic model, and calculate the catalyst recirculation amount based on the process real-time measurement data

K sampling instant, said first catalyst recirculation amount computing module are pressed the said catalyst recirculation amount of regenerating valve discharge characteristic Model Calculation G according to ivp (k) and Δ P (k) that step (1) obtains _C(k):

$G_C\left(k\right)=\mathrm{Cv}\left(\mathrm{Ivp}\left(k\right)\right)\&times;\sqrt{\mathrm{\&Delta;\; P}\left(k\right)}$ Formula 0-1

G _C(k): k sampling instant, by the catalyst recirculation amount that regenerating valve discharge characteristic Model Calculation obtains, kg/h, Cv (ivp (k)): about the unknown function of regenerating valve aperture ivp, m ³/ kg can adopt methods such as polynomial function, neural network, SVMs to approach, and adopts polynomial function to be similar in the present embodiment:

$\mathrm{Cv}\left(\mathrm{Ivp}\left(k\right)\right)=\underset{n=0}{\overset{N}{\mathrm{\&Sigma;}}}{a}_n(k-1)\&times;{\mathrm{Ivp}}^n\left(k\right)$ Formula 0-2

N ∈ [2,5] is the polynomial function order, a _n(k-1) be the multinomial coefficient of k-1 sampling instant, get N=2 among the embodiment, and the initial value of multinomial coefficient is a ₀(0)=-15.915, a ₁(0)=1.159, a ₂(0)=0.058;

3, step 3: catalytic cracking reaction-regenerating section stable state is judged and the multinomial coefficient update calculation

Step (a 3.1) k sampling instant, the T that obtains according to step (1) _R2(k) and T ₂(k), judge whether catalytic cracking reaction-regenerating section reaches stable state, if satisfy formula 0-3:

$|X\left(k\right)-\underset{k_n=1}{\overset{k_N}{\mathrm{\&Sigma;}}}\frac{X(k-k_n)}{k_N}|\&le;\mathrm{\&epsiv;}$ Formula 0-3

Think that then reaction-regenerating section reaches stable state, wherein: vectorial X (k-k _n), k _n=1,2 ..., k _NBe by k-k _nThe T of individual sampling instant _R2(k-k _n) and T ₂(k-k _n) bivector formed, k _N∈ [5,20] is used for data length, gets k among the embodiment _N=10, ε is the stable state decision threshold, gets an enough little positive constant, get among the embodiment ε=[1,4] ',

Step (3.2) changes said step (3.3) over to if reaction-regenerating section reaches stable state, utilizes more new valve regenerating valve model coefficient of regenerator heat Balance Calculation catalyst recirculation amount, if do not reach stable state, keeps the multinomial coefficient in the step (2) constant, even a _n(k)=a _n(k-1), n=0 ..., N changes said step (4) then over to and calculates,

Said second the catalyst recirculation amount computing module of step (3.3) is according to the T that obtains in the step (1) ₁(k) and T ₂(k), by formula 0-4 calculates the catalyst recirculation amount

${\hat{G}}_C\left(k\right)=\frac{Q_C\left(k\right)}{{\mathrm{Cp}}_{\mathrm{Cat}}\&times;(T_2\left(k\right)-T_1\left(k\right))}$ Formula 0-4

k sampling instant; The catalyst recirculation amount that obtains by the regenerator heat Balance Calculation; Kg/h

Cp _Cat: catalyzer specific heat, kJ/ (kg. ℃), given value,

Q _C(k)=Q _O(k)-Q _a(k)-Q _T(k)-Q _q(k): k sampling instant, catalyzer is taken away heat, kJ/h,

K sampling instant, the unit interval is always burnt thermal discharge, kJ/h, Δ H _Cb: the heat that burning 1kg carbon produces, kJ/ (kg carbon), given value, v _Oc: burning 1kg carbon oxygen utilization, m ³/ (kg carbon), given value,

Q _a(k)=F _a(k) ρ _AirCp _Air(T _Fg(k)-T _a(k)): k sampling instant, air is taken away heat, kJ/h, ρ _Air: atmospheric density, kg/m ³, given value, Cp _Air: air specific heat, kJ/ (kg. ℃), given value, Q _T(k)=0.063 * Q _O(k): k sampling instant, desorption heat, kJ/h, Q _q(k)=F _Ex(k) Cp _Water(T _{Ex_out}(k)-T _{Ex_in}(k)): k sampling instant, the heat that the regeneration heat collector is taken away, kJ/h, Cp _Water: chilled water specific heat, kJ/ (kg. ℃), given value,

One group of data that step (3.4) makes

obtains being used to upgrade multinomial coefficient are utilized the said multinomial coefficient of RLS step of updating (3) of band forgetting factor then, and it is following to upgrade iterative formula:

formula 0-5

K (k) is the coefficient correction gain matrix of k sampling instant, and P (k) is a k covariance matrix constantly, and its initial value P (0)=p * I, p get an enough big positive constant, get p=10 among the embodiment ⁵,

λ (k) ∈ [0.92,1] is a forgetting factor, gets λ (k)=0.98 among the embodiment; θ (k)=[a ₀(k), a ₁(k) ..., a _N(k)] ' be the multinomial coefficient of k sampling instant,

Said first catalyst recirculation amount computing module of step (3.5) is according to the multinomial coefficient a that upgrades _n(k) recomputate the catalyst recirculation amount:

And send said dcs DCS to show and the subsequent calculations use;

4, step 4: calculate total reaction heat based on the riser dynamic mathematical models

According to the distribution of temperature point on the riser, said riser is divided into the N section, calculate the reaction heat of riser respectively to each section then, at present embodiment riser is divided into 2 sections, i=1,2, calculate the reaction heat of these 2 sections risers,

Step (4.1) is set the model of a said riser:

$M{p}_i\frac{{\mathrm{DT}}_{\mathrm{Ri}}}{\mathrm{Dt}}+(G_{\mathrm{Oil}}{\mathrm{Cp}}_{\mathrm{Oil}}+G_C{\mathrm{Cp}}_{\mathrm{Cat}}+G_w{\mathrm{Cp}}_w)(T_{\mathrm{Ri}}-T_{r(i-1)})-H_{\mathrm{Lossi}}=G_{\mathrm{Oil}}{H}_{\mathrm{Ri}}$ Formula 0-6

T _Ri: the outlet temperature of i section riser, ℃,

T _R0=T _{R (i-1)}, i=1: the temperature in of the 1st section riser, ℃,

Mp _i: the thermal capacity of i section riser material and wall, kJ/ ℃, given value,

Cp _Oil: oil gas specific heat, kJ/ (kg. ℃), given value,

Cp _Cat: catalyzer specific heat, kJ/ (kg. ℃), given value,

Cp _w: water vapour thermal capacity, kJ/kg, given value,

C _C: by the catalyst recirculation amount that the regenerating valve Model Calculation obtains, kg/h,

H _Lossi: the thermal loss of i section riser, kJ/h, given value,

H _n: the reaction heat of i section riser, kJ/kg feedstock oil,

Step (4.2) discretize riser model equation obtains the reaction heat that the reaction heat computing formula is calculated said each section riser:

$H_{\mathrm{ri}}\left(k\right)=(1-\mathrm{\&gamma;})H_{\mathrm{ri}}(k-1)+\mathrm{\&gamma;}({\mathrm{Mp}}_i\frac{T_i\left(k\right)-T_i(k-1)}{T_s}+H_{\mathrm{oi}}\left(k\right)-H_{\mathrm{lossi}}\left(k\right))/G_{\mathrm{oil}}$

Formula 0-7

H _Oi(k)=(G _Oil(k) Cp _Oil+ G _C(k) Cp _Cat+ G _w(k) Cp _w) (T _Ri(k)-T _{R (i-1)}(k)), 0＜γ≤1st, filtering parameter can be adjusted according to actual conditions, gets γ=0.97 among the embodiment, and the total reaction heat that finally obtains two-stage riser does $H_r=(k+1)=\underset{j=1}{\overset{2}{\mathrm{\&Sigma;}}}{H}_{\mathrm{Rj}}(k+1);$

5, step 5: design riser self-adaptation nonlinear predictive controller

Step (5.1) is set up the riser nonlinear mathematical model towards dynamic control:

$\left\{\begin{array}{c}\frac{{\mathrm{<figure-callout\; id="1"\; label="DT\; r"\; filenames="HSA00000159643200021.png"\; state="\{\{state\}\}">DT</figure-callout>}}_r}{\mathrm{Dt}}=-\frac{1}{s_{T1}(1+{\mathrm{\&Gamma;}}_1)}(T_{r1}-T_{r0})-\frac{{\mathrm{\&Lambda;}}_1}{s_{T1}(1+{\mathrm{\&Gamma;}}_1)}\frac{G_C}{G_{\mathrm{<figure-callout\; id="1"\; label="Oil\; C\; c"\; filenames="HSA00000159643200021.png"\; state="\{\{state\}\}">Oil</figure-callout>}}}{C}_c{1}_{}\\ \frac{{\mathrm{DT}}_{r2}}{\mathrm{Dt}}=-\frac{1}{s_{T2}(1+{\mathrm{\&Gamma;}}_2)}(T_{r2}-T_{r1})-\frac{{\mathrm{\&Lambda;}}_2}{s_{T2}(1+{\mathrm{\&Gamma;}}_2)}\frac{G_C}{G_{\mathrm{Oil}}}(C_{c2}-C_{c1})\end{array}\right.$ Formula 0-8

$s_{\mathrm{Ti}}=\frac{V_{\mathrm{Ri}}{\mathrm{\&rho;}}_{\mathrm{Oil}}}{G_{\mathrm{Oi}}}:$ Oil gas is in the hold-up time of i section riser, h,

V _Ri: i section riser volume, m ³, given value,

ρ _Oil: the hydrocarbon density of the 1st section riser porch, kg/m ³, given value,

${\mathrm{\&Gamma;}}_i=\frac{\frac{V_{\mathrm{Li}}{\mathrm{\&rho;}}_L}{V_{\mathrm{Ri}}{\mathrm{\&rho;}}_{\mathrm{Oil}}}{\mathrm{Cp}}_m}{{\mathrm{Cp}}_{\mathrm{Oil}}+\frac{G_C}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_{\mathrm{Cat}}+\frac{G_w}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_w}:$ Dimensionless thermal capacitance correction coefficient,

V _Li: i section riser antiwear heat resisting layer volume, m ³, given value,

ρ _L: riser antiwear heat resisting layer density, kg/m ³, given value,

Cp _m: the specific heat of riser antiwear heat resisting layer, kJ/ (kg. ℃), given value,

G _C: by the catalyst recirculation amount that the regenerating valve Model Calculation obtains, kg/h,

${\mathrm{\&Lambda;}}_i=\frac{\mathrm{\&Delta;}{H}_{\mathrm{CR}}}{{\mathrm{Cp}}_{\mathrm{Oil}}+\frac{G_C}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_{\mathrm{Cat}}+\frac{C_w}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_w}:$ The ratio of reaction heat and material specific heat, ℃,

Δ H _CR: reaction heat, the kJ/kg catalyzed carbon, given value,

C _Ci: the catalyzed carbon content of i section riser exit catalyzer, %, given value

Cp _{Oil_L}: feedstock oil specific heat, kJ/ (kg. ℃), given value,

Δ H _v: the feedstock oil heat of gasification, kJ/kg feedstock oil, given value,

Step (5.2) adopts the Levenberg-Marquardt optimized Algorithm to find the solution following optimization problem k sampling instant:

$\underset{j=1,...L_2}{\underset{T_{\mathrm{oil}}(k+l_2),\mathrm{ivp}(k+l_2)}{\min }}J=\underset{{\mathrm{<figure-callout\; id="1"\; label="l"\; filenames="HSA00000159643200021.png"\; state="\{\{state\}\}">l</figure-callout>}}_1=1}{\overset{L_1}{\mathrm{\&Sigma;}}}{\left[\begin{array}{c}{H}_r(k+l_1)-H_r^{*}\\ T_{r2}(k+l_1)-T_{r2}^{*}\end{array}\right]}^{\&prime;}Q\left[\begin{array}{c}{H}_r(k+l_1)-H_r^{*}\\ T_{r2}(k+l_1)-T_{r2}^{*}\end{array}\right]$

$+\underset{l_2=1}{\overset{L_2}{\mathrm{\&Sigma;}}}{\left[\begin{array}{c}{T}_{\mathrm{oil}}(k+l_2)-T_{\mathrm{oil}}(k+l_2-1)\\ \mathrm{ivp}(k+l_2)-\mathrm{ivp}(k+l_2-1)\end{array}\right]}^{\&prime;}R\left[\begin{array}{c}{T}_{\mathrm{oil}}(k+l_2)-T_{\mathrm{oil}}(k+l_2-1)\\ \mathrm{ivp}(k+l_2)-\mathrm{ivp}(k+l_2-1)\end{array}\right]$

Formula 0-9

Obtain T _Oil(k+l ₂), ivp (k+l ₂), l ₂=1 ..., L ₂, get prediction step L in the present embodiment ₁=10, control step-length L ₂=2; H _r(k+l ₁) be according to (5.1) said riser model prediction k+l ₁Reaction calorific value constantly, T _R2(k+l ₁) be according to (5.1) said riser model prediction k+l ₁The 2nd section riser outlet temperature value constantly, T _Oil(k+l ₂) be k+l ₂The predicted value of fuel oil preheating temperature constantly, ivp (k+l ₂) be k+l ₂The predicted value of regenerating valve aperture is constantly got the output variable weighting matrix

The control variable weighting matrix

The T that said riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module obtains step (5.2) _Oil(k+1) and ivp (k+1) respectively as the k+1 constantly setting value and the setting value of regenerating valve aperture of fuel oil preheating temperature PID controller, change the k+1 moment then over to, repeated execution of steps (2)-(7).

Claims (1)

1. catalytic cracking reaction depth is characterized in that at line computation and self-adaptation nonlinear prediction control method, on control computer, realizes according to following steps:

Step (1) control computer initialization

Be provided with: first catalyst recirculation amount computing module, second catalyst recirculation amount computing module, regenerating valve model coefficient correction module, riser reaction heat computing module and riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module;

Step (2) is provided with the SI T of control computer _s, sampling period K * Ts, sampling instant k=1; 2; K, said control computer utilizes real-time data base to obtain field data from dcs DCS online, comprises at least: regenerating valve aperture measurement value ivp (k); Regenerating valve drop measurement value Δ P (k), the catalyst temperature measured value T of entering regenerator ₁(k), flow out the catalyst temperature measured value T of regenerator ₂(k), the temperature in measured value T of first section riser _R0(k), the outlet temperature measured value T of first section riser _R1(k), the outlet temperature measured value T of second section riser _R2(k), feedstock oil charging mass flow measurement G _Oil(k), feedstock oil feeding preheating measured temperature T _Oil(k), promote steam mass flow measured value G _w(k), advance riser and promote vapor (steam) temperature measured value T _w(k), advance regenerator volume of air flow measurements F _a(k), oxygen content of smoke gas measured value O _Fg(k), regenerated flue gas measured temperature T _Fg(k), get into the air themperature measured value T of regenerator _a(k), regeneration heat collector cooling water flow measured value F _Ex(k), the regeneration heat collector cooling water inlet measured temperature T of place _{Ex_in}(k), the regeneration heat collector coolant outlet measured temperature T of place _{Ex_out}And the total reaction heat setting value of given two-stage riser (k), With second section riser outlet temperature setting value

Step (a 3) k sampling instant, said first catalyst recirculation amount computing module are pressed regenerating valve discharge characteristic Model Calculation catalyst recirculation amount G according to ivp (k) and Δ P (k) that step (2) obtains _C(k):

$G_C\left(k\right)=\mathrm{Cv}\left(\mathrm{Ivp}\left(k\right)\right)\&times;\sqrt{\mathrm{\&Delta;\; P}\left(k\right)},$ Wherein:

G _C(k): k sampling instant, by the catalyst recirculation amount that regenerating valve discharge characteristic Model Calculation obtains, kg/h, Cv (ivp (k)): about the unknown function of regenerating valve aperture ivp, m * h, adopt polynomial function to be similar to:

$\mathrm{Cv}\left(\mathrm{Ivp}\left(k\right)\right)=\underset{n=0}{\overset{N}{\mathrm{\&Sigma;}}}{a}_n(k-1)\&times;{\mathrm{Ivp}}^n\left(k\right),$ Wherein:

N=2 is the polynomial function order, a _n(k-1) be the multinomial coefficient of k-1 sampling instant, its initial value is a ₀(0)=-15.915, a ₁(0)=1.159, a ₂(0)=0.058;

The said regenerating valve model coefficient of step (4) correction module is pressed the described multinomial coefficient of following step step of updating (3) successively,

Step (a 4.1) k sampling instant, the T that obtains according to step (2) _R2(k) and T ₂(k), judge whether catalytic cracking reaction-regenerating section reaches stable state, if satisfy following condition:

$|X\left(k\right)-\underset{k_n=1}{\overset{k_N}{\mathrm{\&Sigma;}}}\frac{X(k-k_n)}{k_N}|\&le;\mathrm{\&epsiv;},$

Think that then reaction-regenerating section reaches stable state, wherein: vectorial X (k-k _n), k _n=1,2 ..., k _N, be by k-k _nThe T of individual sampling instant _R2(k-k _n) and T ₂(k-k _n) bivector formed, data length k _N=10, ε=[1,4] ' be threshold value,

Step (4.2) changes said step (4.3) over to if catalytic cracking reaction-regenerating section reaches stable state, utilizes regenerator thermal equilibrium formula to calculate the catalyst recirculation amount and upgrades the regenerating valve model coefficient, if do not reach stable state, then makes a _n(k)=a _n(k-1), n=0 ..., N changes step (5) over to and calculates,

Said second the catalyst recirculation amount computing module of step (4.3) is according to the T that obtains in the step (2) ₁(k) and T ₂(k), press regenerator thermal equilibrium formula and calculate the catalyst recirculation amount

${\hat{G}}_C\left(k\right)=\frac{Q_C\left(k\right)}{{\mathrm{Cp}}_{\mathrm{Cat}}\&times;(T_2\left(k\right)-T_1\left(k\right))},$ Wherein:

K sampling instant is by the catalyst recirculation amount that regenerator thermal equilibrium formula calculates, kg/h, Cp _Cat: catalyzer specific heat, kJ/ (kg. ℃), given value,

Q _C(k)=Q _O(k)-Q _a(k)-Q _T(k)-Q _q(k): k sampling instant, catalyzer is taken away heat, kJ/h,

K sampling instant, the unit interval is always burnt thermal discharge, kJ/h, Δ H _Cb: the heat that burning 1kg carbon produces, kJ/ (kg carbon), given value, v _Oc: burning 1kg carbon oxygen utilization, m ³/ (kg carbon), given value,

Q _a(k)=F _a(k) ρ _AirCp _Air(T _Fg(k)-T _a(k)): k sampling instant, air is taken away heat, kJ/h, ρ _Air: atmospheric density, kg/m ³, given value, Cp _Air: air specific heat, kJ/ (kg. ℃), given value, Q _T(k)=0.063 * Q _O(k): k sampling instant, desorption heat, kJ/h, Q _q(k)=F _Ex(k) Cp _Water(T _{Ex_out}(k)-T _{Ex_in}(k)): k sampling instant, regeneration heat collector heat-obtaining amount, kJ/h, Cp _Water: chilled water specific heat, kJ/ (kg. ℃), given value,

One group of data

that step (4.4) makes obtains being used to upgrade multinomial coefficient are utilized the said multinomial coefficient of RLS step of updating (3) then, and it is following to upgrade iterative formula:

wherein:

K (k) is the coefficient correction gain matrix of k sampling instant, and P (k) is a k covariance matrix constantly, its initial value P (0)=10 ⁵* I, I are unit matrix,

λ (k)=0.98 is a forgetting factor, θ (k)=[a ₀(k), a ₁(k) ..., a _N(k)] ' and be the multinomial coefficient of k sampling instant, subscript symbol " ' " representing matrix transposition, down together,

Said first catalyst recirculation amount computing module of step (4.5) is according to the multinomial coefficient a that upgrades _n(k) recomputate the catalyst recirculation amount And send said dcs DCS to show and the subsequent calculations use;

Step (5) riser reaction heat computing module is divided at said riser under two sections the condition, i=1, and 2, calculate the reaction heat of each section riser,

Step (5.1) is set the model of a said riser:

${\mathrm{Mp}}_i\frac{{\mathrm{DT}}_{\mathrm{Ri}}}{\mathrm{Dt}}+(G_{\mathrm{Oil}}{\mathrm{Cp}}_{\mathrm{Oil}}+G_C{\mathrm{Cp}}_{\mathrm{Cat}}+G_w{\mathrm{Cp}}_w)(T_{\mathrm{Ri}}-T_{r(i-1)})-H_{\mathrm{Lossi}}=G_{\mathrm{Oil}}{H}_{\mathrm{Ri}},$ Wherein:

T _Ri: the outlet temperature of i section riser, ℃,

T _R0=T _{R (i-1)}, i=1: the temperature in of the 1st section riser, ℃,

Mp _i: the thermal capacity of i section riser material and wall, kJ/ ℃, given value,

Cp _Oil: oil gas specific heat, kJ/ (kg. ℃), given value,

Cp _Cat: catalyzer specific heat, kJ/ (kg. ℃), given value,

Cp _w: water vapour thermal capacity, kJ/kg, given value,

G _C: by the catalyst recirculation amount that the regenerating valve Model Calculation obtains, kg/h,

H _Lossi: the thermal loss of i section riser, kJ/h, given value,

H _Ri: the reaction heat of i section riser, kJ/kg feedstock oil,

Step (5.2) discretize riser model equation obtains the reaction heat that the reaction heat computing formula is calculated said each section riser:

$H_{\mathrm{Ri}}\left(k\right)=(1-\mathrm{\&gamma;})H_{\mathrm{Ri}}(k-1)+{\mathrm{\&gamma;}}^{({\mathrm{Mp}}_i\frac{T_i\left(k\right)-T_i(k-1)}{T_s}+H_{\mathrm{Oi}}\left(k\right)-H_{\mathrm{Lossi}}\left(k\right))}/G_{\mathrm{Oil}},$ Wherein:

H _Oi(k)=(G _Oil(k) Cp _Oil+ G _C(k) Cp _Cat+ G _w(k) Cp _w) (T _Ri(k)-T _{R (i-1)}(k)), 0＜γ≤1st, filtering parameter, the total reaction heat that finally obtains two-stage riser does

Step (6) is k sampling instant, and said riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module is carried out reaction depth control by following step,

Step (6.1) is set up the riser nonlinear dynamic mathematical model towards dynamic control:

$\left\{\begin{array}{c}\frac{{\mathrm{DT}}_{r1}}{\mathrm{Dt}}=-\frac{1}{s_{T1}(1+{\mathrm{\&Gamma;}}_1)}(T_{r1}-T_{r0})-\frac{{\mathrm{\&Lambda;}}_1}{s_{T1}(1+{\mathrm{\&Gamma;}}_1)}\frac{G_C}{G_{\mathrm{Oil}}}{C}_{c1}\\ \frac{{\mathrm{DT}}_{r2}}{\mathrm{Dt}}=-\frac{1}{s_{T2}(1+{\mathrm{\&Gamma;}}_2)}(T_{r2}-T_{r1})-\frac{{\mathrm{\&Lambda;}}_2}{s_{T2}(1+{\mathrm{\&Gamma;}}_2)}\frac{G_C}{G_{\mathrm{Oil}}}(C_{c2}-C_{c1})\end{array}\right.,$ Wherein:

oil gas is in the hold-up time of i section riser; H

V _Ri: i section riser volume, m ³, given value,

ρ _Oil: the hydrocarbon density of the 1st section riser porch, kg/m ³, given value,

${\mathrm{\&Gamma;}}_i=\frac{\frac{V_{\mathrm{Li}}{\mathrm{\&rho;}}_L}{V_{\mathrm{Ri}}{{\mathrm{\&rho;}}_{\mathrm{Oil}}}^m}\mathrm{Cp}}{{\mathrm{Cp}}_{\mathrm{Oil}}+\frac{G_C}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_{\mathrm{Cat}}+\frac{G_w}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_w}:$ Dimensionless thermal capacitance correction coefficient,

V _Li: i section riser antiwear heat resisting layer volume, m ³, given value,

ρ _L: riser antiwear heat resisting layer density, kg/m ³, given value,

Cp _m: the specific heat of riser antiwear heat resisting layer, kJ/ (kg. ℃), given value,

G _C: by the catalyst recirculation amount that the regenerating valve Model Calculation obtains, kg/h,

${\mathrm{\&Lambda;}}_i\frac{{\mathrm{\&Delta;\; H}}_{\mathrm{CR}}}{{\mathrm{Cp}}_{\mathrm{Oil}}+\frac{G_C}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_{\mathrm{Cat}}+\frac{G_w}{G_{\mathrm{Oil}}}{\mathrm{Cp}}_w}:$ The ratio of reaction heat and material specific heat, ℃,

Δ H _CR: reaction heat, the kJ/kg catalyzed carbon, given value,

C _Ci: the catalyzed carbon content of i section riser exit catalyzer, %, given value

Cp _{Oil_L}: feedstock oil specific heat, kJ/ (kg. ℃), given value,

Δ H _v: the feedstock oil heat of gasification, kJ/kg feedstock oil, given value,

Step (6.2) is k sampling instant; Riser reaction depth self-adaptation nonlinear PREDICTIVE CONTROL module is through control fuel oil preheating temperature and regenerating valve aperture; Setting value

and

promptly adopt the Levenberg-Marquardt optimized Algorithm to find the solution following optimization problem to make second section riser outlet temperature and reaction heat reach separately:

$\underset{\underset{j=1,...L_2}{T_{\mathrm{oil}}(k+l_2),\mathrm{ivp}(k+l_2)}}{\min }J=\underset{l_1=1}{\overset{L_1}{\mathrm{\&Sigma;}}}{\left[\begin{array}{c}{H}_r(k+l_1)-H_r^{*}\\ T_{r2}(k+l_1)-T_{r2}^{*}\end{array}\right]}^{\&prime;}Q\left[\begin{array}{c}{H}_r(k+l_1)-H_r^{*}\\ T_{r2}(k+l_1)-T_{r2}^{*}\end{array}\right]$

$+\underset{l_2=1}{\overset{L_2}{\mathrm{\&Sigma;}}}{\left[\begin{array}{c}{T}_{\mathrm{oil}}(k+l_2)-T_{\mathrm{oil}}(k+l_2-1)\\ \mathrm{ivp}(k+l_2)-\mathrm{ivp}(k+l_2-1)\end{array}\right]}^{\&prime;}R\left[\begin{array}{c}{T}_{\mathrm{oil}}(k+l_2)-T_{\mathrm{oil}}(k+l_2-1)\\ \mathrm{ivp}(k+l_2)-\mathrm{ivp}(k+l_2-1)\end{array}\right]$

Controlled amount T _Oil(k+l ₂), ivp (k+l ₂), l ₂=1 ..., L ₂, wherein: L ₁=10 is prediction step, L ₂=2 are control step-length, H _r(k+l ₁) be according to the said riser nonlinear dynamic mathematical model prediction of step (6.1) k+l ₁Reaction calorific value constantly, T _R2(k+l ₁) be according to the said riser nonlinear dynamic mathematical model prediction of step (6.1) k+l ₁The 2nd section riser outlet temperature value constantly, T _Oil(k+l ₂) be k+l ₂The predicted value of fuel oil preheating temperature constantly, ivp (k+l ₂) be k+l ₂The predicted value of regenerating valve aperture constantly,

Be the output variable weighting matrix,

It is the control variable weighting matrix;

The T that the said riser reaction depth of step (7) self-adaptation nonlinear PREDICTIVE CONTROL module obtains step (6.2) _Oil(k+1) and ivp (k+1) respectively as k+1 setting value and the k+1 setting value of regenerating valve aperture constantly of fuel oil preheating temperature PID controller constantly, change k+1 then over to constantly, repeated execution of steps (2)-(7) are until the total K of the sampling that reaches setting.

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