A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

Robust multiple model adaptive control: Modified using ν‐gap metric

A new robust adaptive control method is proposed, which removes the deficiencies of the classic robust multiple model adaptive control (RMMAC) using benefits of the ν‐gap metric. First, the classic RMMAC design procedure cannot be used for systematic design for unstable plants because it uses the Baram Proximity Measure, which cannot be calculated for open‐loop unstable plants. Next, the %FNARC method which is used as a systematic approach for subdividing the uncertainty set makes the RMMAC structure being always companion with the µ‐synthesis design method. Then in case of two or more uncertain parameters, the model set definition in the classic RMMAC is based on cumbersome ad hoc methods. Several methods based on ν‐gap metric for working out the mentioned problems are presented in this paper. To demonstrate the benefits of the proposed RMMAC method, two benchmark problems subject to unmodeled dynamics, stochastic disturbance input and sensor noise are considered as case studies. The first case‐study is a non‐minimum‐phase (NMP) system, which has an uncertain NMP zero; the second case‐study is a mass‐spring‐dashpot system that has three uncertain real parameters. In the first case‐study, five robust controller design methods (H₂, H_∞, QFT, H_∞ loop‐shaping and µ‐synthesis) are implemented and it is shown via extensive simulations that RMMAC/ν/QFT method improves disturbance‐rejection, when compared with the classic RMMAC. In the second case‐study, two robust controller design methods (QFT and mixed µ‐synthesis) are applied and it is shown that the RMMAC/ν/QFT method improves disturbance‐rejection, when compared with RMMAC/ν/mixed?µ. Copyright © 2010 John Wiley & Sons, Ltd.     

Steam consumption prediction of a gas sweetening process with methyldiethanolamine solvent using machine learning approaches

This study proposes a comprehensive data processing and modeling framework for building high‐accuracy machine learning model to predict the steam consumption of a gas sweetening process. The data pipeline processes raw historical data of this application and identifies the minimum number of modeling variables required for this prediction in order to ease the applicability and practicality of such methods in the industrial units. On the modeling end, an empirical comparison of most of the state‐of‐the‐arts regression algorithms was run in order to find the best fit to this specific case study. The ultimate goal is to leverage this model to identify the achievable energy conservation opportunity in such plants. The historical data for this modeling was collected from a gas treating plant at South Pars Gas Complex for 3 years from 2017 to 2019. This data gets passed through a multistage data processing scheme that conducts multicollinearity analysis and model‐based feature selection. For model selection, a wide range of regression algorithms from different classes of regressor have been considered. Among all these methods, the Gradient Boosting Machines model outperformed the others and achieved the lowest cross‐validation error. The results show that this model can predict the steam consumption values with 98% R‐squared accuracy on the holdout test set. Furthermore, the offline analysis demonstrates that there is a potential of 2% energy saving, equivalent to 24 000 metric tons of annual steam consumption reduction, which can be achieved by mapping the underperforming energy consumption states of the unit to the expected performances predicted by the model.     

Fuzzy error governor: A practical approach to counter actuator saturation on flexible joint robots

In this paper, a practical method to counter actuator saturation based on a fuzzy error governor is developed and a complete case study is considered. In addition to good performance, the method has two attracting properties: It does not change the structure of the main controller, and therefore, the theoretically proven characteristics of the system are untouched, and it is simply implementable in practice. The proposed controller structure is applied on a flexible joint robot (FJR). The robust stability of the closed loop system for an n-DOF FJR is thoroughly analyzed and the proposed controller is implemented on a laboratory setup to show the ease of implementation and the resulting closed-loop performance. The main controller used for the n-DOF FJR consists of a composite structure, with a PD controller on the fast dynamics and a PID controller on the slow dynamics. The bandwidth of the fast controller is decreased during critical occasions with the fuzzy logic supervisor, which adjusts the loop gain to a proper level. Using Lyapunov direct method, the robust stability of the overall system is analyzed in presence of modeling uncertainties, and it is shown that if the PD and the PID gains are tuned to satisfy certain conditions, the closed loop system becomes UUB stable.     

A SURVEY ON THE CONTROL OF FLEXIBLE JOINT ROBOTS

The robotics literature of the last two decades contains many important advances in the control of flexible joint robots. This is a survey of these advances and an assessment for future developments, concentrated mostly on the control issues of flexible joint robots.     

Three-dimensional numerical modelling of a fast fluidized bed biomass gasifier to generate energy from wastes

The modelling of a biomass fluidized bed gasification system, one of the most effective ways to produce energy from biomass resources and wastes, has been performed in this study. The effect of the turbulence phenomena, including calculations relating to flow turbulence, chemical fuel reactions, and energy and momentum exchange between multiple solid and gas phases, has been taken into account in the current research as a novel approach. A computational fluid dynamics case study model that combines equations with comprehensive geometry has been considered. Results have been compared with published operational records of an existing power plant to validate the model. The solid particle distribution, the velocity of the mixture and gas phase, the turbulent flow viscosity ratio, and the temperature distribution in the model indicated the accuracy of the simulation performance compared with the experimental studies. The production of the molar fraction of the constituent elements of the synthesis gas has been evaluated in transient conditions. Additionally, 35 s after the process began, the system's performance was estimated, and the results indicated the average molecular weights of hydrogen, carbon monoxide, carbon dioxide, and methane are 26%, 23%, 12.5%, and 3.3%, respectively, which presented high precision with the experimental results.     

A new sliding mode controller for wind turbine pitch angle control

This paper focuses on variable-speed wind turbines where control is aimed at stabilizing the output power. A hybrid control algorithm is proposed which includes a new arrangement of two controllers along with an observer. Estimation of power coefficient via sliding mode observer reduces the amount of errors caused by changing the power coefficient parameter in different turbines. Moreover, the use of this special arrangement of sliding mode controller, proportional-integral (PI) controller and sliding mode observer, removes one of the controller parameter denoting wind speed from the designed algorithm, which in turn eliminates disturbances related to wind speed changes. In order to show the efficiency of the proposed control method, the performances of the controllers are evaluated by simulation in MATLAB software.