PID-based control of a single-link flexible manipulator in vertical motion with genetic optimisation

This paper presents an investigation into dynamic simulation and controller optimization based on genetic algorithms (GAs) for a single-link flexible manipulator system in vertical plane motion. The dynamic model of the system is derived using the Lagrange equation and discretised using the finite difference (FD) method. GA optimization is used to optimize the parameters of the proportional-integral-derivative (PID) based controllers for control of rigid-body and flexible motion dynamics of the system. The important point is to evaluate the range of PID parameter which used in the GAs programmed to find the best value of this parameter. Comparative performance assessment of the control approaches are presented and discussed in the time and the frequency domains

Modelling and Optimization of Primary Steam Reformer System Case Study: the Primary Reformer PT Petrokimia Gresik Indonesia

Steam reforming of hydrocarbons has been in use as the principal process for the generation of hydrogen and synthesis gas needed in the Ammonia production in petrochemical industries. Optimal operation of existing steam reformers is crucial in view of the high energy consumption and large value addition involved in the process. The economic objective of the process is determined by the cost of gas (Methane), the cost of steam and additional fuel. An optimum steam to gas ratio is expected from an optimum process control.  This can be applied on ratio control parameter for natural gas feed. In addition, steam Carbon Ratio is used to decrease coke formation in catalyst reformer. This paper presents the model identification and optimization of reforming control system of an industrial Primary Reformer at PT. Petrokimia Gresik (One of the fertilizer petrochemical industry in Indonesia). The reformer model has been approximated in the form of Takagi-Sugeno-Kang fuzzy inference system, with architecture in neural-network model. ANFIS (Adaptive Neuro-Fuzzy Inference System) has been utilized to determine NARX or ARX parameters model describing the dynamic of Industrial operational data have been used for training and validating the model. The optimization problem has been addressed through the utilization of Constrained Nonlinear Programming. The aim is to find the optimal process and ratio controller parameters to achieve the maximum Hydrogen formation. For a maximum fixed production rate of hydrogen produced by the unit, minimization of methane feed rate is chosen as the objective function to meet processing requirements.Keywords: Hydrocarbons, Model identification, ANFI

Optimal Energy Consumption Analysis of Natural Gas Pipeline

There are many compressor stations along long-distance natural gas pipelines. Natural gas can be transported using different boot programs and import pressures, combined with temperature control parameters. Moreover, different transport methods have correspondingly different energy consumptions. At present, the operating parameters of many pipelines are determined empirically by dispatchers, resulting in high energy consumption. This practice does not abide by energy reduction policies. Therefore, based on a full understanding of the actual needs of pipeline companies, we introduce production unit consumption indicators to establish an objective function for achieving the goal of lowering energy consumption. By using a dynamic programming method for solving the model and preparing calculation software, we can ensure that the solution process is quick and efficient. Using established optimization methods, we analyzed the energy savings for the XQ gas pipeline. By optimizing the boot program, the import station pressure, and the temperature parameters, we achieved the optimal energy consumption. By comparison with the measured energy consumption, the pipeline now has the potential to reduce energy consumption by 11 to 16 percent

Advanced constraints management strategy for real-time optimization of gas turbine engine transient performance

Motivated by the growing technology of control and data processing as well as the increasingly complex designs of the new generation of gas turbine engines, a fully automatic control strategy that is capable of dealing with different aspects of operational and safety considerations is required to be implemented on gas turbine engines. An advanced practical control mode satisfaction method for the entire operating envelope of gas turbine engines is proposed in this paper to achieve the optimal transient performance for the engine. A constraint management strategy is developed to generate different controller settings for short-range fighters as well as long-range intercontinental aircraft engines at different operating conditions by utilizing a model predictive control approach. Then, the designed controller is tuned and modified with respect to different realistic considerations including the practicality, physical limitations, system dynamics, and computational efforts. The simulation results from a verified two-spool turbofan engine model and controller show that the proposed method is capable of maneuverability and/or fuel economy optimization indices while satisfying all the predefined constraints successfully. Based on the parameters, natural frequencies, and dynamic behavior of the system, a set of optimized weighting factors for different engine parameters is also proposed to achieve the optimal and safe operation for the engine at different flight conditions. The paper demonstrates the effects of the prediction length and control horizon; adding new constraints on the computational effort and the controller performance are also discussed in detail to confirm the effectiveness and practicality of the proposed approach in developing a fully automatic optimized real-time controller for gas turbine engines

Dynamic optimization of a gas-liquid reactor

The final publication is available at Springer via http://dx.doi.org/10.1007/s10910-011-9941-1A dynamic gas-liquid transfer model without chemical reaction based on unsteady film theory is considered. In this case, the mathematical model presented for gas-liquid mass-transfer processes is based on mass balances of the transferred substance in both phases. The identificability property of this model is studied in order to confirm the possible identifiable parameters of the model from a given set of experimental data. For that, a different modeled of the system is given. A procedure for the identification is proposed. On the other hand, the aim of this work is to solve the quadratic optimal control problem, using an explicit representation of the model. The problem includes some results on controllability, observability and stability criteria and the relation between these properties and the parameters of the model. Using the optimal control problem we study the stability of the system and show how the choice of the weighting matrices can improve the behavior of the system but with an increase of the energy control cost. © 2011 Springer Science+Business Media, LLC.This work has been partially supported by PAID-05-10-003-295 and by MTM2010-18228.Cantó Colomina, B.; Cardona Navarrete, SC.; Coll, C.; Navarro-Laboulais, J.; Sánchez, E. (2012). Dynamic optimization of a gas-liquid reactor. Journal of Mathematical Chemistry. 50(2):381-393. https://doi.org/10.1007/s10910-011-9941-1S381393502Bayón L., Grau J.M., Ruiz M.M., Suárez P.M.: Initial guess of the solution of dynamic optimization of chemical processes. J. Math. Chem. Model. 48, 28–37 (2010)Ben-Zvi A., McLellan P.J., McAuley K.B.: Ind. Eng. Chem. Res. 42, 6607–6618 (2003)Cantó B., Coll C., Sánchez E.: Structural identifiability of a model of dialysis. Math. Comp. Model. 50, 733–737 (2009)Cantó B., Coll C., Sánchez E.: Identifiability of a class of discretized linear partial differential algebraic equations. Math. Probl. Eng. 2011, 1–12 (2011)Craciun G., Pantea C.: Identifiability of chemical reaction networks. J. Math. Chem. 44, 244–259 (2008)Dai L.: Descriptor Control Systems. Springer, New York (1989)Deckwer W.D.: Bubble Column Reactors. Wiley, Chichester (1992)Kantarci N., Borak F., Ulgen K.O.: Bubble column reactors. Proc. Biochem. 40(7), 2263–2283 (2005)Kawakernaak H., Sivan R.: Linear Optimal Control Systems. Wiley-Interscience, New York (1972)Kuo B.C.: Automatic Control Systems, 6th edn. Prentice-Hall, Englewood Cliffs (1991)Navarro-Laboulais J., Cardona S.C., Torregrosa J.I., Abad A., López F.: Practical identifiability analysis in dynamic gas-liquid reactors. Optimal experimental design for mass-transfer parameters determination. Comp. Chem. Eng. 32, 2382–2394 (2008)Navarro-Laboulais J., López F., Torregrosa J.I., Cardona S.C., Abad A.: Transient response, model structure and systematic errors in hybrid respirometers: structural identifiabilit analysis based on OUR and DO measurements. J. Math. Chem. 44(4), 969–990 (2007)Patel R., Munro N.: Multivariable Systen. Theory and Design. Pergamon Press, New York (1982)Sondergeld K.: A generalization of the Routh–Hurwitz stability criteria and a application to a problem in robust controller design. IEEE Trans. Automat. Contr. AC-28(10), 965–970 (1983

Efficient Dynamic Compressor Optimization in Natural Gas Transmission Systems

The growing reliance of electric power systems on gas-fired generation to balance intermittent sources of renewable energy has increased the variation and volume of flows through natural gas transmission pipelines. Adapting pipeline operations to maintain efficiency and security under these new conditions requires optimization methods that account for transients and that can quickly compute solutions in reaction to generator re-dispatch. This paper presents an efficient scheme to minimize compression costs under dynamic conditions where deliveries to customers are described by time-dependent mass flow. The optimization scheme relies on a compact representation of gas flow physics, a trapezoidal discretization in time and space, and a two-stage approach to minimize energy costs and maximize smoothness. The resulting large-scale nonlinear programs are solved using a modern interior-point method. The proposed optimization scheme is validated against an integration of dynamic equations with adaptive time-stepping, as well as a recently proposed state-of-the-art optimal control method. The comparison shows that the solutions are feasible for the continuous problem and also practical from an operational standpoint. The results also indicate that our scheme provides at least an order of magnitude reduction in computation time relative to the state-of-the-art and scales to large gas transmission networks with more than 6000 kilometers of total pipeline

How to Model Condensate Banking in a Simulation Model to Get Reliable Forecasts? Case Story of Elgin/Franklin

Imperial Users onl

Control vector parameterization with sensitivity based refinement applied to baking optimization

In bakery production, product quality attributes as crispness, brownness, crumb and water content are developed by the transformations that occur during baking and which are initiated by heating. A quality driven procedure requires process optimization to improve bakery production and to find operational procedures for new products. Control vector parameterization (CVP) is an effective method for the optimization procedure. However, for accurate optimization with a large number of parameters CVP optimization takes a long computation time. In this work, an improved method for direct dynamic optimization using CVP is presented. The method uses a sensitivity based step size refinement for the selection of control input parameters. The optimization starts with a coarse discretization level for the control input in time. In successive iterations the step size was refined for the parameters for which the performance index has a sensitivity value above a threshold value.With this selection, optimization is continued for a selected group of input parameters while the other nonsensitive parameters (below threshold) are kept constant. Increasing the threshold value lowers the computation time, however the obtained performance index becomes less. A threshold value in the range of 10–20% of the mean sensitivity satisfies well. The method gives a better solution for a lower computation effort than single run optimization with a large number of parameters or refinement procedures without selection

Modelling of a Gas Cap Gas Lift System

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Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system