Figure - available from: International Journal of Chemical Engineering
This content is subject to copyright. Terms and conditions apply.
Source publication
Burner failures are common abnormal conditions associated with industrial fired heaters. Preventing from economic loss and major equipment damages can be attained by compensating the lost heat due to burners' failures, which can be possible by defining appropriate setpoints to rearrange the firing rates for healthy burners. In this study, artificia...
Similar publications
Faced with the problems of the environment and resources, the co-combustion of sewage sludge has gradually become a trend. In this paper, the combination of thermogravimetric and Fourier transform infrared spectroscopy (TG-FTIR) was used to study the co-combustion of sewage sludge (SS) and coffee industry residues (CIR). SS and CIR were mixed accor...
Citations
... It was concluded that the framework performed well and successfully predicted the outlet temperature under variabilities in the inlet temperature [34]. To recover an industrial fired-heater furnace from aberrant states, an ANN model was constructed to estimate the optimal set points for the combustion control system [35]. In another study, ANN was used to predict changes in the composition of refinery gas-fired combustion products [36]. ...
The current work developed an integrated framework of artificial intelligence and genetic algorithm (GA) for the furnace of a petroleum refinery to predict the optimum amount of excess air and mass flow rates of crude oil and fuel stream in the presence of uncertainty in process conditions. A furnace model was built in Aspen Exchanger Design and Rating (EDR) using industrial data. Then interface between the furnace model and MATLAB was established using the com server. Data points were generated by varying the crude oil composition as well as the inlet temperature and pressure of cold crude oil, fuel, and air stream through ±1%, ±2%, ±3%, ±4%, and ±5% change in their original values. Then GA was used to determine the optimal amount of excess air and mass flow rates of crude oil and fuel for each data point. In total, 360 data points were generated and 70 % of the data set was utilized for training an Artificial Neural Networks (ANN) model while the remaining data points were evenly distributed for its validation and testing. The proposed ANN model achieved high performance with a correlation coefficient of 0.99984. Furthermore, exergy analysis of the standalone furnace model, the GA based optimized model, and the ANN integrated model was performed. The GA based approach outperformed the standalone model in terms of exergetic efficiency. On the hand, the ANN based framework achieved comparable exergetic efficiency to the GA based approach within significantly lesser period of time.
... It was concluded that the framework performed well and successfully predicted the outlet temperature under variabilities in the inlet temperature [34]. To recover an industrial fired-heater furnace from aberrant states, an ANN model was constructed to estimate the optimal set points for the combustion control system [35]. In another study, ANN was used to predict changes in the composition of refinery gas-fired combustion products [36]. ...
... In the environment produced by burning natural gas with an excess of air, oxidation is the most important phenomenon, as treated by Orozco et al. [8], who showed, in the ferritic ASTM A335 P92 steel, the formation of an adherent and continuous oxidized layer rich in Cr, which avoids other corrosive phenomena such as carburization. Furthermore, it is important to keep the flame length as short as possible, because an incorrect regulation of the fire heaters operating parameters [9] causes flame impingement on the tube surfaces giving rise to local overheating. ...
A calculation model is proposed to assess the combined effects of hot oxidation and creep damage on the residual life of radiant tubes used in topping furnaces of petrochemical plants. Experimental measures of thickness loss due to hot oxidation were carried out on specimens made of ASTM A335 P5 steel for tubes, to define the correlation of the thickness reduction with operating temperature and time, which was subsequently applied to evaluate the residual life in creep conditions. The model resulted effective in formulating hypotheses on the tubes actual thermal history, and defining the range of operating conditions that guarantee a safe return to service. It has also proved effective to predict the service life span of new tubes in ideal stable operating temperature, confirming the need for scheduling periodic checks to detect the actual condition of the tubes. Finally, a procedure for decision-making on tube decommissioning in maintenance interventions is presented.
In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control System (SCS) are proposed to improve the efficiency, safety, availability, and control capabilities of industrial furnaces. The main components of ICS are process control systems and advanced control systems that consist of overheating protection and load control. New soft sensors are designed as a combination of Laguerre filters and an artificial neural network to estimate the surface temperature of the furnace’s tubes, which allows the protection system to adjust fuel flow rate via overriding commands. Model-based fault detection systems are developed to detect faults in the combustion system and fouling in the furnace tubes and prepare features for the supervisory system. The supervisory control system is responsible for interfering between different components, evaluating the situation, and decision making based on the unit status and process conditions. An intuitionistic fuzzy inference system is employed as the core of the supervisory controller to tolerate disturbance and faults by switching the control modes. Test studies using experimental data of the furnace indicate the capability of the proposed monitoring and control system to operate in various loading situations and recover the system from abnormal conditions.
Note to Practitioners
—In petrochemical industries, several reports have been issued about the load reduction of fired-heater furnaces imposed by combustion system faults and emergency shutdowns to carry out un-planned repairs due to fouling and wax-formation in tubes. Different activities such as detecting abnormal conditions, identifying faults, and enforcing corrective action can be performed by operators through manual actions. This paper is focused on designing a new supervisory control system (SCS) to be able to recover the fired heater furnace from abnormal conditions and keep running the plant. The SCS evaluates the condition of the unit by acquiring information from main variables, sensors, actuators, operating status of components and utilities, and operator commands. By identifying the root cause of faults, SCS makes decision on recognizing hazard degree, raising alarms, and applying automatic corrective actions.
