Fluidized Catalytic Cracking (FCC) is a complex process that arises due to feed composition, non-linearities, and dynamic mass and heat interactions in its components. FCC is difficult to model and monitor in industries, and one of the key reasons is that they are multivariable processes. Such processes are highly interacting and that makes the process of controlling even more difficult. The interaction between loops can be quantified easily by dRGA. An easy and effective way of controlling multivariable processes is to implement a centralized control system, considering the interactions between measured and manipulated variables. In this study, a centralized control system is designed for the riser section of the FCC unit. The dRGA method is modified to enhance the closed-loop response by formulating an optimization problem and obtaining an optimal controller settings. A rigorous simulation studies show an 826% reduction in ISE values, a 309% reduction in IAE values, and a 262% reduction in ITAE value of from the dRGA method to the modified dRGA method. Further, IAE values for are reduced by 29% from dRGA to modified dRGA method and 34% from synthesis to modified dRGA method.
This paper presents the simultaneous optimization of the design and operation in nominal conditions of a geothermal plant where the geothermal fluid is split into two streams to feed an Organic Rankine Cycle (ORC) and a District Heating Network (DHN). The topology of the DHN is also investigated. A Mixed Integer Non-Linear Programming (MINLP) optimization problem is formulated and solved using the GAMS software in order to determine the ORC sizing and the DHN topology. In this study, only R-245fa is used as ORC working fluid, an optional Internal Heat Exchanger (IHE) is considered in the ORC and consumers in DHN can be definite or optional. A multi-objective optimization is performed by maximizing the annual net profit and minimizing the total exergy losses in the plant. The weighted sum of objective functions is used to solve the problem. By varying the weight factor, a Pareto front is obtained and the distance to the ideal, but infeasible, solution enabled to choose the best compromise. Four different DHN topologies are observed depending on the weight factor. Using a suitable criterion to make a decision, the selected configuration corresponds to the most expanded DHN with the smallest value of profit. A sensitive analysis shows that, in case of lower geothermal temperature, it is possible to obtain a unique DHN topology whatever the weight factor.
A new principle of UHVDC Line pilot protection based on internal parameters of the trigger angle of converter is proposed, in order to improve the ability of the protection to withstand transition resistance. Through the analysis of UHVDC control system, it is found that, due to the regulation of control system, in terms of trigger delay angle of rectifier side and trigger leading angle of inverter side, the change tendency in internal fault and external fault is different. Thus, the protection criterion is constructed. Compared with the traditional protection principle using voltage and current to establish protection criteria, this principle uses internal parameters as protection quantity. The simulation based on PSCAD/EMTDC verifies the effectiveness of the protection principle.
Nanofluids has significant effect on heat transfer enhancement for comparatively high Reynolds number than to low Reynolds number flow. Whereas, vibration effects reduces in significance as Reynolds number increases. This study combined these two method of heat transfer enhancement i.e. use of nanofluid flow through pipe under vibration. A grid independent CFD model used for the study was validated in various aspects such as it was validated for variation of local Nusselt number, isothermal vibrational flow and non-isoviscous viscosity model so that one could believe the results obtained from the model. A valid CFD simulations has been done to investigate the effect on heat transfer to fluid flowing from pipe subjected to a constant heat flux. Al2O3-water based nanofluid was used as Newtonian fluid as it exhibits Newtonian behavior at low concentration (∅ < 2%). In order to make it non-Newtonian in nature, mixture of Al2O3 nanoparticles and 0.5 wt% aqueous CMC solution was used. Temperature dependent viscosity and thermal conductivity relations were considered for nanofluid so that it can be effectively model as single phase fluid including factors like liquid layering, Brownian motion etc. Simulations were done for different Reynolds number, volume fraction and solid particle diameter and results were presented in the form of ratio of heat transfer coefficient of vibration flow to steady-state flow. At low Reynolds number flow, a significant increment was observed for non-Newtonian nanofluid and its effect increases for volume fraction and solid particle than that of Newtonian nanofluid for the range of simulation parameters used.
The present work describes a method of automatic fault detection and identification based on a hybrid model (HM): First Principles – Neural Network. The FPM can simulate a wide range of situations while the NN corrects the model output using information from the historical data of the process. Operating conditions corresponding to different types of faults were simulated with the HM and saved with their description in a process state library. To detect a fault, the online measured data was compared with that corresponding to the operation under normal conditions. If a significant deviation was detected, the current state was compared with all the states stored in the process state library and it was identified as the one at the shortest distance. The method was tested with real data from a methanol-water industrial distillation column. During the studied period of operation of the plant, two faults were identified and reported. The proposed method was able to identify such failures more effectively than an equivalent model of first principles. The results obtained show that the proposed method has a great potential to be used in the automatic diagnosis of faults in refining and petrochemical processes.