SOFT SENSORS APPLICATION FOR CRUDE DISTILLATION UNIT PRODUCT QUALITY ESTIMATION

Zbog nemogućnosti kontinuiranog mjerenja svojstava frakcionacijskih produkata kolone za atmosfersku destilaciju, razvijeni su softverski senzori za procjenu kvalitete produkata. Softverski senzori su razvijeni za procjenu kraja destilacije petroleja kad je 95 % goriva predestiliralo koristeći linearne i nelinearne metode identificiranja. Eksperimentalni podaci su prikupljeni s distribuiranog sustava za vođenje (DCS) i obuhvaćaju kontinuirano mjerene varijable i laboratorijska mjerenja. U radu je prikazan razvoj ARMAX (engl. AutoRegressive Moving Average with eXogenous inputs) modela, NARX (engl. Nonlinear AutoRegressive model with eXogenous inputs) modela i HW (Hammerstein-Wiener) modela. Kako bi se izbjegao dugotrajan postupak odabira optimalnih parametara modela, metodom pokušaja i pogreške u svrhu optimiranja primijenjeni su genetički algoritmi. Razvijeni modeli softverskih senzora mogu se rabiti za kontinuirano procjenjivanje svojstava goriva te za primjenu metoda inferencijskog vođenja.Fractionation product properties of the crude distillation unit (CDU) need to be monitored and controlled through feedback mechanism. Due to the inability of on-line measurement, soft sensors for product quality estimation are developed. Soft sensors for kerosene 95% distillation point are developed using linear and nonlinear identification methods. Experimental data are acquired from the refinery distributed control system (DCS) and include on-line available continuously measured variables and laboratory assays. In present work development of AutoRegressive Moving Average with eXogenous inputs (ARMAX), Nonlinear AutoRegressive model with eXogenous inputs (NARX) and Hammerstein-Wiener (HW) model are presented. To overcome the problem of selecting the best model parameters by trial and error procedure, genetic algorithms were used for determining the best model parameters. Based on developed soft sensors it is possible to estimate fuel properties continuously by embedding model in DCS on site as well as applying the methods of inferential control

SOFT SENSORS APPLICATION FOR CRUDE DISTILLATION UNIT PRODUCT QUALITY ESTIMATION

Zbog nemogućnosti kontinuiranog mjerenja svojstava frakcionacijskih produkata kolone za atmosfersku destilaciju, razvijeni su softverski senzori za procjenu kvalitete produkata. Softverski senzori su razvijeni za procjenu kraja destilacije petroleja kad je 95 % goriva predestiliralo koristeći linearne i nelinearne metode identificiranja. Eksperimentalni podaci su prikupljeni s distribuiranog sustava za vođenje (DCS) i obuhvaćaju kontinuirano mjerene varijable i laboratorijska mjerenja. U radu je prikazan razvoj ARMAX (engl. AutoRegressive Moving Average with eXogenous inputs) modela, NARX (engl. Nonlinear AutoRegressive model with eXogenous inputs) modela i HW (Hammerstein-Wiener) modela. Kako bi se izbjegao dugotrajan postupak odabira optimalnih parametara modela, metodom pokušaja i pogreške u svrhu optimiranja primijenjeni su genetički algoritmi. Razvijeni modeli softverskih senzora mogu se rabiti za kontinuirano procjenjivanje svojstava goriva te za primjenu metoda inferencijskog vođenja.Fractionation product properties of the crude distillation unit (CDU) need to be monitored and controlled through feedback mechanism. Due to the inability of on-line measurement, soft sensors for product quality estimation are developed. Soft sensors for kerosene 95% distillation point are developed using linear and nonlinear identification methods. Experimental data are acquired from the refinery distributed control system (DCS) and include on-line available continuously measured variables and laboratory assays. In present work development of AutoRegressive Moving Average with eXogenous inputs (ARMAX), Nonlinear AutoRegressive model with eXogenous inputs (NARX) and Hammerstein-Wiener (HW) model are presented. To overcome the problem of selecting the best model parameters by trial and error procedure, genetic algorithms were used for determining the best model parameters. Based on developed soft sensors it is possible to estimate fuel properties continuously by embedding model in DCS on site as well as applying the methods of inferential control

Calculation of the Optimal Cooling Temperature Profile of the Batch Crystalliser

Cilj rada bio je izraditi računalni program koji služi za proračun optimalnog temperaturnog profila hlađenja šaržnog kristalizatora. Kao modelni sustav za istraživanje procesa uzet je kalijev nitrat otopljen u vodi. Za izradu matematičkog modela procesa primijenjene su populacijske bilance, odnosno njihova momentna transformacija. Za dobivanje optimalnog temperaturnog profila primijenjena je diskretizacija temperaturnog profila uz globalni algoritam optimizacije. Za provođenje optimizacije primijenjen je genetički algoritam, dok je sustav običnih diferencijalnih jednadžbi rješavan metodom Runge-Kutta 4,5. Funkcija cilja bila je minimiziranje omjera trećeg momenta sekundarnom nukleacijom nastalih kristala i trećeg momenta kristala cjepiva na kraju procesa. U radu je najprije ispitan utjecaj uvjeta zaustavljanja genetičkog algoritma na vrijeme proračuna i vrijednost funkcije. Nakon što je određen optimalni uvjet zaustavljanja, ispitan je utjecaj broja točaka diskretizacije temperaturnog profila na iznos funkcije cilja i potrebno vrijeme proračuna. Ustanovljeno je da je optimalni uvjet za zaustavljanje proračuna kad petnaest članova generacije imaju funkcije cilja koje se ne razlikuju više od tolerancije. Ustanovljeno je da se optimalno rješenje dobiva podjelom temperaturnog profila na osam dijelova. Da bi se ispitala ponovljivost proračuna za optimalne uvjete, proračun je ponavljan devet puta. Optimalni temperaturni profil uspoređen je s linearnim hlađenjem istog trajanja. Rezultati simulacijskih eksperimenata ukazuju na znatno poboljšanje procesa primjenom optimalnog temperaturnog profila naspram linearnog.The aim of this work was to create a computer program that can be used to calculate the optimal cooling temperature profile of the batch crystalliser. Potassium nitrate dissolved in water was used as a model system for process research. To create a mathematical model of the process, population balances were used, i.e., their moment transformation. To obtain the optimal temperature profile, a discretisation of the temperature profile was performed using a global optimisation algorithm. A genetic algorithm was used for the optimisation, while a system of ordinary differential equations was solved using the Runge-Kutta 4,5 method. The objective function was to minimise the ratio between the third moment of crystals produced by secondary nucleation, and the third moment of seed crystals at the end of the process. Firstly, the influence of the stopping conditions of the genetic algorithm on the computation time and the value of the objective function was tested. After the optimal stopping condition was determined, the influence of the number of discretisation points of the temperature profile on the value of the objective function and the required computation time was investigated. It was found that the optimal stopping condition was when fifteen members of a generation had objective function values that did not differ by more than the tolerance. It is shown that the optimal solution was achieved by dividing the temperature profile into eight parts. To check the repeatability of the calculation for optimal conditions, the calculation was repeated nine times. The optimal temperature profile was compared to a linear cooling of the same duration to determine the benefits of optimisation. The results of the simulation experiments indicate a significant improvement in the process when using the optimal temperature profile compared to the linear profile

MODELS FOR CONTIONUOS ESTIMATION OF BENZENE IN REFORMATE

U rafinerijskim postrojenjima je radi ograničenja sadržaja benzena u gorivima neophodno kontinuirano pratiti sadržaj benzena u lakom i teškom reformatu. Kako je procesni analizator koji procjenjuje sadržaj benzena često u kvaru, razvijeni su modeli softverskih senzora za kontinuiranu procjenu sadržaja benzena u reformatu. Softverski senzori razvijeni su primjenom viševeličinskih linearnih metoda identifikacije procesa. Razvijeni su linearni dinamički modeli: model konačnog impulsnog odziva [engl. Finite Impulse Response (FIR)] i model izlazne pogreške [engl. Output Error (OE)]. Za pronalaženje najbolje strukture dinamičkih modela primijenjeni su genetički algoritmi, čime su nadograđene i poboljšane postojeće metode razvoja modela softverskih senzora. Razvijeni modeli pokazali su zadovoljavajuću točnost pri usporedbi s rezultatima s postrojenja na skupu podataka za vrednovanje modela. Na postrojenju su implementirani FIR i OE model za procjenu sadržaja benzena u lakom reformatu.Due to environmental regulations and production requirement the benzene content in fuels need to be limited. Therefore, it is necessary to continuously monitor the benzene content in light and heavy reformate. As the process analyzers that measure the benzene content in reformate, are often out of service, models of soft sensor are developed for the continuous estimation of benzene content. Soft sensors are developed using linear identification methods and global optimization methods. The development of Finite Impulse Response (FIR)) model and Output Error (OE) model are presented. To overcome the problem of selecting the best model order for multiple input models, by trial and error, genetic algorithms (GA) was used which makes the development of the soft sensors more systematic. Developed models show a satisfactory match with analyzer data on a validation data set. Models are implemented on the fractionation plant for the estimation of benzene content in light reformate

MODELS FOR CONTIONUOS ESTIMATION OF BENZENE IN REFORMATE

U rafinerijskim postrojenjima je radi ograničenja sadržaja benzena u gorivima neophodno kontinuirano pratiti sadržaj benzena u lakom i teškom reformatu. Kako je procesni analizator koji procjenjuje sadržaj benzena često u kvaru, razvijeni su modeli softverskih senzora za kontinuiranu procjenu sadržaja benzena u reformatu. Softverski senzori razvijeni su primjenom viševeličinskih linearnih metoda identifikacije procesa. Razvijeni su linearni dinamički modeli: model konačnog impulsnog odziva [engl. Finite Impulse Response (FIR)] i model izlazne pogreške [engl. Output Error (OE)]. Za pronalaženje najbolje strukture dinamičkih modela primijenjeni su genetički algoritmi, čime su nadograđene i poboljšane postojeće metode razvoja modela softverskih senzora. Razvijeni modeli pokazali su zadovoljavajuću točnost pri usporedbi s rezultatima s postrojenja na skupu podataka za vrednovanje modela. Na postrojenju su implementirani FIR i OE model za procjenu sadržaja benzena u lakom reformatu.Due to environmental regulations and production requirement the benzene content in fuels need to be limited. Therefore, it is necessary to continuously monitor the benzene content in light and heavy reformate. As the process analyzers that measure the benzene content in reformate, are often out of service, models of soft sensor are developed for the continuous estimation of benzene content. Soft sensors are developed using linear identification methods and global optimization methods. The development of Finite Impulse Response (FIR)) model and Output Error (OE) model are presented. To overcome the problem of selecting the best model order for multiple input models, by trial and error, genetic algorithms (GA) was used which makes the development of the soft sensors more systematic. Developed models show a satisfactory match with analyzer data on a validation data set. Models are implemented on the fractionation plant for the estimation of benzene content in light reformate

Design, Construction and Control of a Manipulator Driven by Pneumatic Artificial Muscles

This paper describes the design, construction and experimental testing of a single-joint manipulator arm actuated by pneumatic artificial muscles (PAMs) for the tasks of transporting and sorting work pieces. An antagonistic muscle pair is used in a rotational sense to produce a required torque on a pulley. The concept, operating principle and elementary properties of pneumatic muscle actuators are explained. Different conceptions of the system realizations are analyzed using the morphological-matrix conceptual design framework and top-rated solution was practically realized. A simplified, control-oriented mathematical model of the manipulator arm driven by PAMs and controlled with a proportional control valve is derived. The model is then used for a controller design process. Fluidic muscles have great potential for industrial applications and assembly automation to actuate new types of robots and manipulators. Their characteristics, such as compactness, high strength, high power-to-weight ratio, inherent safety and simplicity, are worthy features for advanced manipulation systems. The experiments were carried out on a practically realized manipulator actuated by a pair of muscle actuators set into an antagonistic configuration. The setup also includes an original solution for the subsystem to add work pieces in the working space of the manipulator