Energy-based fault detection for an auto-thermal reformer

Abstract

The effects of the so-called energy-crisis are being felt world-wide. Dwindling natural resources has seen an increase in research efforts to develop new energy efficient technologies, and to manage existing facilities better. As the petroleum, more generally petrochemical, industry (PCI) is one of the main actors in a fuel-based energy environment the efficient operation of these plants is of foremost importance. Innovative maintenance paradigms, such as risk- and condition-based maintenance (RBM or CBM), can provide a vast improvement in overall operational efficiency of petrochemical (PC) plants, but is rarely implemented. This lack of implementation can be attributed to several factors, chiefly the lack of proper plant models for the monitoring task. Existing techniques for implementing CBM requires either analytical models or makes use of data-driven model derivation techniques. The former is widely considered to be impractical for large PC plants, almost to the point of being impossible. Data-driven methods, on the other hand, are often considered inaccurate, and the lack of formal validation methods hampers the trustworthiness of the derived models. Monitoring of a PC plant is inherently a multi-domain problem as energy, in the form of natural resources, is converted into a form more suited to the requirements of modern systems. For this reason, energy-based monitoring of the PC plant would make sense, at least theoretically; In order to do this, an energy-based representation of the plant is required. Although there are well known energy-based modelling tools (such as Bond graphs), application of these techniques to PC plants pose a significant challenge, both from the modelling, and interpretation perspectives. Du Rand developed a technique for monitoring of a nuclear power plants Brayton cycle that made use of the enthalpy and entropy of various points in the cycle to perform fault detection. In this work, the technique developed by Du Rand is applied to an auto-thermal reformer (ATR). The auto-thermal reformer is widely considered to be the most economically viable reforming technology and is also the first unit operation in a gas-to-liquids (GTL) process it is thus of critical importance. The primary contribution of this work shows that Du Rands method breaks down when applied to the ATR of a GTL process. Most notably it fails to identify changes in chemical composition of the product (synthesis gas). With the concepts developed by Du Rand as input, exergy was investigated and considered to be more suitable for monitoring of the ATR. Exergy closely models the intuitive understanding of the physical world, in that it can be created, stored, transferred, and notably destroyed. Physical exergy, the component associated with physical properties (temperature, flow rates, pressure) is a mathematical combination of enthalpy and entropy and this closely follows Du Rands approach. However, chemical exergy also takes into account the amount of substance present, and, perhaps more importantly, the usefulness of said substance. Thus, chemical exergy allows for the detection of compositional variations, which for petrochemical applications of fault detection would be critical. The identification of Exergy (both physical and chemical) as a usable modelling domain for petrochemical process plants is considered to be a secondary contribution. It is expected that this will also have specific advantages in terms of hierarchical modelling and a reduction in the computational complexity typically associated with fault detection.