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Desulfurization control of natural gas has long been a challenging industrial issue owing to its inherent difficulty in establishing accurate mathematical model for the nonlinear and strong coupling process. In this paper, a data-based adaptive dynamic programming (ADP) algorithm is presented to solve optimal control for natural gas desulfurization...
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... system consist of several components: desulfurization unit, dehydration unit, sulfur recovery unit, and tail gas treatment unit. Specially, the desulfurization unit is the key for the natural gas purification. Generally, there are two instruments in a desulfurization unit, i.e., feed gas filter and absorption tower, which is shown in Fig. ...
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... They then presented an improved adaptive dynamic programming method to solve the optimal control problem for the desulfurization system. 7 Monedero et al designed and developed a decision system based on a module of two neural networks using past operating points for energy optimization of a petrochemical plant. According to the results, the potential of increasing energy efficiency in the selected plant was around 7%. 8 In addition to predictive modeling, another key aspect of this operational workflow is to find the process reference points or values of controller variables in the control system in order to reduce energy consumption. ...
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 Nm 3 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. K E Y W O R D S data-driven operational control, energy consumption reduction, machine learning, natural gas sweetening
... In [19], an event-triggered approximate optimal control structure is proposed for a nonlinear continuous time system with control constraints. In [20] addressed the challenging industrial problem of natural gas desulfurization control, and proposed an improved unscented kalman filter assisted ADP method to solve the optimal control problem of the desulfurization system. Hence, data-driven adaptive control method, which can accurately identify the complex system and achieve optimal control, is widely used in actual industrial system [21]. ...
... Theorem 1: Consider (12) the nominal system described by the value function (15), suppose Assumption 1-3 holds. Then, the optimal control u * (t) design in (20) can guarantee the system (13) run steadily in the sense of uniform ultimate boundedness (UUB). Proof: By formula (21), we have ...
... This shows that V * (x) is a Lyapunov function for system (13) with the control u * , whenever x (t) lies outside the compact set x. Therefore, the optimal control u * (t) developed in (20) can ensure the trajectory of system (13) to be UUB. ...
Optimal control of aluminum electrolysis production process (AEPP) has long been a challenging industrial issue due to its inherent difficulty in establishing an accurate dynamic model. In this paper, a novel robust optimal control algorithm based on adaptive dynamic programming (ADP) is proposed for the AEPP, where the system subjects to input constraints. First, to establish an accurate dynamic model for the AEPP system, recursive neural network (RNN) is employed to reconstruct the system dynamic using the input-output production data. To ensure input constraints are not to exceed the bound of the actuator, the optimal control problem of the AEPP is formulated under a new nonquadratic form performance index function. Then, considering the perturbation of the AEPP, the robust control problem is effectively converted to the constrained optimal control problem via system transformation. Furthermore, a single critic network framework is developed to obtain the approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. Finally, the proposed ADP controller is applied to the AEPP system to validate the effectiveness and performance.
This dissertation contains two important contributions, as follows: (i) 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 historical data for this modeling was collected from a gas treating plant at South Pars Gas Complex for three years from 2017 to 2019. This data gets passed through a multi-stage 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. (ii) This research also presents 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; (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. 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.
In wet flue gas desulfurization (WFGD) process, the pH value of the absorption tower slurry is a crucial factor to the efficiency of desulfurization system. Aiming at the nonlinearity and large lag of the pH change in WFGD process, a predictive control strategy based on Hammerstein–Wiener inverse model compensation is proposed. During the calculation of optimal control, an anti‐model of Wiener nonlinearity unit is adopted to transform the output setting values and sampling values. Similarly in the control process, the controller output is applied to the actual controlled object after inverse transformation of the static nonlinear Hammerstein model. Through the above two inverse transformations, the controller output is identical with the input of linear link in the closed‐loop system. In this article, the inverse model compensation method is utilized to transform nonlinear process control into linear system control, avoiding the large computation of nonlinear model optimization. Finally, the feasibility and effectiveness of the proposed scheme are verified by simulation.
