Estimation of Iron Ore Pellet Softening in a Blast Furnace with Computational Thermodynamics

In blast furnaces it is desirable for the burden to hold a lumpy packed structure at as high a temperature as possible. The computational thermodynamic software FactSage (version 7.2, Thermfact/CRCT, Montreal, Canada and GTT-Technologies, Aachen, Germany) was used here to study the softening behavior of blast furnace pellets. The effects of the main slag-forming components (SiO2, MgO, CaO and Al2O3) on liquid formation were estimated by altering the chemical composition of a commercial acid pellet. The phase equilibria for five-component FeO-SiO2-CaO-MgO-Al2O3 systems with constant contents for three slag-forming components were computed case by case and the results were used to estimate the formation of liquid phases. The main findings of this work suggested several practical means for the postponement of liquid formation at higher temperatures: (1) reducing the SiO2 content; (2) increasing the MgO content; (3) reducing the Al2O3 content; and (4) choosing suitable CaO contents for the pellets. Additionally, the olivine phase (mainly the fayalitic type) and its dissolution into the slag determined the amount of the first-formed slag, which formed quickly after the onset of softening. This had an important effect on the acid pellets, in which the amount of the first-formed slag varied between 10 and 40 wt.%, depending on the pellets’ SiO2 content.

Active Learning Analytic Coupled with Edge Computing for Intermittent Shut-In Optimization and Carbon Emission Reduction in Shale Gas Reservoirs

Gas production from unconventional shale reservoirs is known for rapid declines. Intermittent shut-in production constitutes a technique typically applied to low-production wells during late life stages to maintain economic rates. This technique involves a cyclic process of shutting in the well temporarily to allow it to build up pressure and subsequently switching the well to production. Operators often manage hundreds of wells on intermittent shut-in production; these wells, however, incur different shut-in and production cycle times, thus requiring a complicated management approach. Because every well has a unique production behavior and reservoir characteristics, searching for optimum operational conditions individually is not only technically challenging, but also operationally time-consuming and labor- intensive. Our goal was to use active learning analytic, a type of machine learning deployed on an edge computing platform, to autonomously control and optimize these unconventional gas wells. The field trial results show increased production, reduced liquid loading, decreased manual intervention, and reduced carbon footprint. Our solution utilizes an edge computing platform to deploy the analytic on the wellhead without requiring a stable internet connection. A computing device at the edge connects to controllers on site, processes data, sets system control parameters, and enables automation for operations deploying an optimization algorithm. Active learning algorithms are valuable for use in the optimization of systems that are not mathematically definable. These algorithms are also proven to learn the relationship between the inputs and outputs and use prior knowledge to intelligently search for the optimum settings within the defined operating limits. The low latency of edge computing allows for high-frequency data collection in seconds and a rapid control of the wells. The edge device continuously monitors production and initiates re- optimization as needed when operational conditions change. We developed an analytic that autonomously controls the intermittent production technique where a well is shut-in based on a specified minimum gas production rate and opened when the pressure builds up to the specified target during the shut-in period. The analytic actively learns and measures the ways in which the specified parameters improve production rates. Additionally, the analytic continuously monitors production data and identifies any well liquid loading events. When liquid loading occurs in the wellbore as observed from the production pattern, the analytic automatically shuts in the well to build up pressure and minimizes additional liquid formation. In the field trial, we deployed the edge analytic to monitor gas production and the specified well shut-in and open conditions for 10 different wells in the Haynesville Shale Play. Analyzing each well in the context of approximately 30 intermittent production cycles (shut- in/open), the analytic successfully mapped the surface response, identified the optimal setting for well shut-in/open conditions, and continuously updated the surface response. Overall, the analytic improved production by 4% and reduced the liquid loading occurrences and manual well unloading events by 94%, resulting in an average reduction of approximately 600 tons of CO2 equivalent per well per year. In summary the active learning analytic was developed and deployed on an edge computing platform to 1) optimize intermittent shut-in by searching for the optimum settings that yield the most gas production; 2) automate the optimization process; and 3) monitor the liquid formation for potential loading events. In this paper, we present a use case for an algorithm adapted for the optimization of a dynamic system such as hydrocarbon production from a well.

Wet Gas Formation and Carryover in Compressor Suction Equipment

In gas processing, boosting, and gathering applications, gas-liquid separator equipment (typically referred to as a scrubber) is placed upstream of each reciprocating compressor stage to remove water and hydrocarbon condensates. However, field experience indicates that liquids are often still present downstream of the separation equipment. When liquids are ingested into the reciprocating compressor, machinery failures, some of which are severe, can result. While it is generally understood that liquid carryover and condensation can occur, it is less clear how the multiphase fluid moves through equipment downstream of the scrubber. In this paper, mechanisms responsible for liquid formation and carryover into reciprocating compressors are explored. First, the effects of liquid ingestion on reciprocating compressors reported in the open literature are reviewed. Then, the role of heat and pressure loss along the gas flow path is investigated to determine whether liquid formation (i.e., condensation) is likely to occur for two identical compressors with different pulsation bottle configurations. For this investigation, conjugate heat transfer (CHT) models of the suction pulsation bottles are used to identify regions where liquid dropout is likely to occur. Results of these investigations are presented. Next, liquid carryover from the upstream scrubber is considered. Multiphase models are developed to determine how the multiphase fluid flows through the complex flow path within the pulsation bottle. Two liquid droplet size distributions are employed in these models. Descriptions of the modeling techniques, assumptions, and boundary conditions are provided.

The Limit Inlet Temperature of Refrigeration Centrifugal Compressor to Avoid Phase Change

From the polytropic compression work formula, we can find that the consumed polytropic work will reduce with the decrease of inlet temperature while compressing the refrigerant to the same compression ratio. However, the refrigerant may condense if the inlet temperature is low enough. Though the principle that the acceleration of fluid may result in condensation has been proved by numerical simulations and experiments, and the liquid formation inside the supercritical carbon dioxide (SCO2) centrifugal compressor has been widely studied, there is still not a user-friendly method to predict whether the inlet condition may cause liquid formation inside the compressor. The fluid flow in the space near the blade suction face of the leading edge (SNSL) is assumed to the similar flow in a converging nozzle when the mass flow is larger enough; the fluid impinges on the suction surface of blades, and the absolute velocity of fluid will not be greater than sound velocity. The fluid turns to impinge on the pressure surface with the decrease of mass flow rate, which is similar to the flow in a converging-diverging nozzle, and the maximum absolute velocity in the SNSL may be greater than the sound speed. A method is proposed to predict the lowest inlet temperature of refrigeration centrifugal compressor to avoid phase change, which is called the limit temperature. The predicted lowest temperature shares the same trend with the numerical results. The condensation will occur inside the compressor when the inlet temperature is lower than the limit inlet temperature. The lowest temperature will first increase and then decrease as the mass flow increases, which should be taken into account while designing a refrigeration centrifugal compressor or adjusting the operating condition.

Overcoming solid handling issues in continuous flow substitution reactions through ionic liquid formation

Continuous flow reactions, often plagued by precipitation and clogging problems, can be easily performed by selecting bases that form ionic liquids upon protonation.