Predicting Pyrolysis of a Wide Variety of Petroleum Coke Using an Independent Parallel Reaction Model and a Backpropagation Neural Network

In this work, the pyrolysis behavior and gaseous products of petroleum coke were investigated by nonisothermal thermogravimetric analysis (TGA) and thermogravimetry-mass spectrometry (TG-MS). Then, the pyrolysis kinetics of six kinds of petroleum coke (Fushun (FS), Fuyu (FY), Wuhan (WH), Zhenhai (ZH), Qilu (QL), and Shijiazhuang (SJZ)) were determined by an independent parallel reaction (IPR) model, and the kinetic parameters (activation energy and preexponential factor) were obtained. In addition, an efficient backpropagation neural network (BPNN) was developed to predict the thermal data of six kinds of petroleum coke. The BPNN-predicted thermal data were used to calculate the kinetic parameters based on the IPR model, and the results were compared with the ones calculated using experimental data. The results showed that the pyrolysis process of six kinds of petroleum coke was divided into three stages, of which stage II (250-900 °C) had the significant mass loss, corresponding to the devolatilization of petroleum coke. MS fragmented ion intensity analysis indicated that the main pyrolysis products were methane CH _x (m/z = 13, 14, 15, and 16), aliphatic hydrocarbon C₃H₅, H₂, CO, CO₂, and H₂O. The thermal data predicted by the IPR, BPNN, and BPNN-IPR (BPNN combined with IPR) models were in good agreement with the experimental data. Most importantly, it was concluded that the BPNN-predicted data can be further applied to calculate the kinetic parameters using the IPR kinetic model.

Thermodynamic Design of Organic Rankine Cycle (ORC) Based on Petroleum Coke Combustion

Thermodynamic analysis of Organic Rankine Cycle (ORC) was performed in this work. The Petroleum Coke burner provided the required heat flux for the Butane Boiler. The simulation of pet-coke combustion was carried out by using Fire Dynamics Simulator software (FDS) version 5.0. Validation of the FDS calculation results was carried out by comparing the temperature of the gaseous mixture and CO2 mole fractions to the literature. It was discovered that they are similar to those reported in the literature. An Artificial Intelligence (AI) time forecasting analysis was performed on this work. The AI algorithm was applied to the temperature and soot sensor readings. Two Python libraries were applied in order to forecast the time behaviour of the thermocouple readings: Statistical model—ARIMA (Auto-Regressive Integrated Moving Average) and KERAS—deep learning library. ARIMA is a class of model that captures a suite of different standard temporal structures in time series data. Keras is a python library applied for deep learning and runs on top of Tensor-Flow. It has been developed in order to perform deep learning models as fast and easily as possible for research and development. The model accuracy and model loss plot shows comparable performance (train and test). Butane has been employed as a working fluid in the ORC. Butane is considered one of the best pure fluids in terms of exergy efficiency. It has low specific radiative forcing (RF) compared to Ethane and Propane. Moreover, it has zero ozone depletion potential and low Global Warming Potential. It is considered flammable, highly stable and non-corrosive. The thermodynamic properties of Butane needed to evaluate the heat rate and the power were calculated by applying the ASIMPTOTE online thermodynamic calculator. It was shown that the calculated net power of the ORC cycle is similar to the net power reported in the literature (relative error of 4.8%). The proposed ORC energetic system obeys the first and second laws of thermodynamics. The thermal efficiency of the cycle is 20.4%.

Multiphysics Design of Pet-Coke Burner and Hydrogen Production by Applying Methane Steam Reforming System

Pet-coke (petroleum coke) is identified as a carbon-rich and black-colored solid. Despite the environmental risks posed by the exploitation of pet-coke, it is mostly applied as a boiling and combusting fuel in power generation, and cement production plants. It is considered as a promising replacement for coal power plants because of its higher heating value, carbon content, and low ash. A computational fluid dynamics (CFD) computational model of methane steam reforming was developed in this research. The hydrogen production system is composed from a pet-coke burner and a catalyst bed reactor. The heat released, produced by the pet-coke combustion, was utilized for convective and radiative heating of the catalyst bed for maintaining the steam reforming reaction of methane into hydrogen and carbon monoxide. This computational algorithm is composed of three steps—simulation of pet-coke combustion by using fire dynamics simulator (FDS) software coupled with thermal structural analysis of the burner lining and a multiphysics computation of the methane steam reforming (MSR) process taking place inside the catalyst bed. The structural analysis of the burner lining was carried out by coupling the solutions of heat conduction equation, Darcy porous media steam flow equation, and structural mechanics equation. In order to validate the gaseous temperature and carbon monoxide mole fraction obtained by FDS calculation, a comparison was carried out with the literature results. The maximal temperature obtained from the combustion simulation was about 1440 °C. The calculated temperature is similar to the temperature reported, which is also close to 1400 °C. The maximal carbon dioxide mole fraction reading was 15.0%. COMSOL multi-physics software solves simultaneously the catalyst media fluid flow, heat, and mass with chemical reaction kinetics transport equations of the methane steam reforming catalyst bed reactor. The methane conversion is about 27%. The steam and the methane decay along the catalyst bed reactor at the same slope. Similar values have been reported in the literature for MSR temperature of 510 °C. The hydrogen mass fraction was increased by 98.4%.