MODELLING AND CONTROL OF ERSW PROCESSES BY NEURONAL NETWORK

A study on the use of artificial intelligence (AI) techniques for the modelling and subsequent control of an electric resistance spot welding process (ERSW) is presented. The ERSW process is characterized by the coupling of thermal, electrical, mechanical, and metallurgical phenomena. For this reason, early attempts to model it using computational methods established as the methods of finite differences, finite element, and finite volumes, ask for simplifications that lead the model obtained far from reality or very costly in terms of computational costs, to be used in a real-time control system. In this sense, the authors have developed an ERSW controller that uses fuzzy logic to adjust the energy transferred to the weld nugget. The proposed control strategies differ in the speed with which it reaches convergence. Moreover, their application for a quality control of spot weld through artificial neural networks (ANN) is discussed.

A biomimetic piezoelectric pump: Computational and experimental characterization

Flow pumps have been developed for classical applications in Engineering, and are important instruments in areas such as Biology and Medicine. Among applications for this kind of device we notice blood pump and chemical reagents dosage in Bioengineering. Furthermore, they have recently emerged as a viable thermal management solution for cooling applications in small-scale electronic devices. This work presents the performance study of a novel principle of a piezoelectric flow pump which is based oil the use of a bimorph piezoelectric actuator inserted in fluid (water). Piezoelectric actuators have some advantages over classical devices, such as lower noise generation and ease of miniaturization. The main objective is the characterization of this piezoelectric pump principle through computational simulations (using finite element software), and experimental tests through a manufactured prototype. Computational data, Such as flow rate and pressure curves, have also been compared with experimental results for validation purposes. (C) 2009 Elsevier B.V. All rights reserved.

Deterministic mathematical model for the prediction of the as-cast macrostructure

A multiphase deterministic mathematical model was implemented to predict the formation of the grain macrostructure during unidirectional solidification. The model consists of macroscopic equations of energy, mass, and species conservation coupled with dendritic growth models. A grain nucleation model based on a Gaussian distribution of nucleation undercoolings was also adopted. At some solidification conditions, the cooling curves calculated with the model showed oscillations (""wiggles""), which prevented the correct prediction of the average grain size along the structure. Numerous simulations were carried out at nucleation conditions where the oscillations are absent, enabling an assessment of the effect of the heat transfer coefficient on the average grain size and columnar-to-equiaxed transition.

Numerical modeling of ductile crack extension in high pressure pipelines with longitudinal flaws

This study examines the applicability of a micromechanics approach based upon the computational cell methodology incorporating the Gurson-Tvergaard (GT) model and the CTOA criterion to describe ductile crack extension of longitudinal crack-like defects in high pressure pipeline steels. A central focus is to gain additional insight into the effectiveness and limitations of both approaches to describe crack growth response and to predict the burst pressure for the tested cracked pipes. A verification study conducted on burst testing of large-diameter, precracked pipe specimens with varying crack depth to thickness ratio (a/t) shows the potential predictive capability of the cell approach even though both the CT model and the CTOA criterion appear to depend on defect geometry. Overall, the results presented here lend additional support for further developments in the cell methodology as a valid engineering tool for integrity assessments of pipelines with axial defects. (C) 2011 Elsevier Ltd. All rights reserved,

Estimating wave spectra from the motions of moored vessels: Experimental validation

The practicability of estimating directional wave spectra based on a vessel`s 1st order response has been recently addressed by several researchers. Different alternatives regarding statistical inference methods and possible drawbacks that could arise from their application have been extensively discussed, with an apparent preference for estimations based on Bayesian inference algorithms. Most of the results on this matter, however, rely exclusively on numerical simulations or at best on few and sparse full-scale measurements, comprising a questionable basis for validation purposes. This paper discusses several issues that have recently been debated regarding the advantages of Bayesian inference and different alternatives for its implementation. Among those are the definition of the best set of input motions, the number of parameters required for guaranteeing smoothness of the spectrum in frequency and direction and how to determine their optimum values. These subjects are addressed in the light of an extensive experimental campaign performed with a small-scale model of an FPSO platform (VLCC hull), which was conducted in an ocean basin in Brazil. Tests involved long and short crested seas with variable levels of directional spreading and also bimodal conditions. The calibration spectra measured in the tank by means of an array of wave probes configured the paradigm for estimations. Results showed that a wide range of sea conditions could be estimated with good precision, even those with somewhat low peak periods. Some possible drawbacks that have been pointed out in previous works concerning the viability of employing large vessels for such a task are then refuted. Also, it is shown that a second parameter for smoothing the spectrum in frequency may indeed increase the accuracy in some situations, although the criterion usually proposed for estimating the optimum values (ABIC) demands large computational effort and does not seem adequate for practical on-board systems, which require expeditious estimations. (C) 2009 Elsevier Ltd. All rights reserved.

Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Real time optimization (RTO) with model predictive control (MPC)

This paper studies a simplified methodology to integrate the real time optimization (RTO) of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. Optimal conditions of the process at steady state are searched through the use of a rigorous non-linear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model, obtained through a plant step test. The main advantage of the proposed strategy is that the resulting control/optimization problem can still be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed may be comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a non-linear programming problem with a much higher computer load. (C) 2010 Elsevier Ltd. All rights reserved.

Optimizing model predictive control of an industrial distillation column

The main scope of this work is the implementation of an MPC that integrates the control and the economic optimization of the system. The two problems are solved simultaneously through the modification of the control cost function that includes an additional term related to the economic objective. The optimizing MPC is based on a quadratic program (QP) as the conventional MPC and can be solved with the available QP solvers. The method was implemented in an industrial distillation system, and the results show that the approach is efficient and can be used, in several practical cases. (C) 2011 Elsevier Ltd. All rights reserved.

Robust model predictive controller with output feedback and target tracking

A model predictive controller (MPC) is proposed, which is robustly stable for some classes of model uncertainty and to unknown disturbances. It is considered as the case of open-loop stable systems, where only the inputs and controlled outputs are measured. It is assumed that the controller will work in a scenario where target tracking is also required. Here, it is extended to the nominal infinite horizon MPC with output feedback. The method considers an extended cost function that can be made globally convergent for any finite input horizon considered for the uncertain system. The method is based on the explicit inclusion of cost contracting constraints in the control problem. The controller considers the output feedback case through a non-minimal state-space model that is built using past output measurements and past input increments. The application of the robust output feedback MPC is illustrated through the simulation of a low-order multivariable system.

Gas flow analysis in a Kraft recovery boiler

Demands for optimal boiler performance and increased concerns in lowering emission have always been the driving force in the reevaluation and evolution of the Kraft boiler: specifically the air distribution strategies that are directly related to achieving increased residence time of flue gas combustion inside the furnace which in turn lowers atmosphere emission levels and enhances boiler operation. This paper presents the results of a study that analyzes the interaction of the different multilevel air injections have on flue gas flow patterns including various quaternary air supply arrangements. Additionally, this study assesses the performance of the CFD (Computational Fluid Dynamics) model against data available in literature. Simulations were performed considering isothermal and incompressible flows, and did not take into account thermal phenomena or chemical reactions. The numerical solutions generated proved to be coherently related to the data available in literature, and provided proof of the efficiency of tertiary level air injection, as well as revealed that quaternary air injection ports arranged in a symmetrical configuration is most suitable for optimal equipment operation. (C) 2010 Elsevier B.V. All rights reserved.

Residence time distribution in holding tubes using generalized convection model and numerical convolution for non-ideal tracer detection

A procedure is proposed for the determination of the residence time distribution (RTD) of curved tubes taking into account the non-ideal detection of the tracer. The procedure was applied to two holding tubes used for milk pasteurization in laboratory scale. Experimental data was obtained using an ionic tracer. The signal distortion caused by the detection system was considerable because of the short residence time. Four RTD models, namely axial dispersion, extended tanks in series, generalized convection and PER + CSTR association, were adjusted after convolution with the E-curve of the detection system. The generalized convection model provided the best fit because it could better represent the tail on the tracer concentration curve that is Caused by the laminar velocity profile and the recirculation regions. Adjusted model parameters were well cot-related with the now rate. (C) 2010 Elsevier Ltd. All rights reserved.

A mechanistic kinetic model for phenol degradation by the Fenton process

The objective of this paper is to develop and validate a mechanistic model for the degradation of phenol by the Fenton process. Experiments were performed in semi-batch operation, in which phenol, catechol and hydroquinone concentrations were measured. Using the methodology described in Pontes and Pinto [R.F.F. Pontes, J.M. Pinto, Analysis of integrated kinetic and flow models for anaerobic digesters, Chemical Engineering journal 122 (1-2) (2006) 65-80], a stoichiometric model was first developed, with 53 reactions and 26 compounds, followed by the corresponding kinetic model. Sensitivity analysis was performed to determine the most influential kinetic parameters of the model that were estimated with the obtained experimental results. The adjusted model was used to analyze the impact of the initial concentration and flow rate of reactants on the efficiency of the Fenton process to degrade phenol. Moreover, the model was applied to evaluate the treatment cost of wastewater contaminated with phenol in order to meet environmental standards. (C) 2009 Elsevier B.V. All rights reserved.

DEVELOPMENT OF STATIC MIXERS FOR MISCIBLE FLUIDS IN LAMINAR FLOW WITH THE USE OF COMPUTATIONAL FLUID DYNAMICS (CFD)

Static mixers with improved performance were developed from CFD simulations in a stepwise approach. The relevant geometric features of simple mixer designs and the corresponding mixing mechanisms-laminar shear, elongational flow, and distributive mixing-were identified first. This information was used to formulate guidelines for the development of new geometries. The solid elements of the static mixer should: (a) provide restrictions to the flow; (b) deflect the flow; (c) be sequentially rotated around the flow direction to provide symmetry; (d) extend from the center of the pipe to the vicinity of the walls to avoid short-circuiting; and (e) distribute and remix the flow. Based on these guidelines, two improved mixer designs were developed: the DS A-I mixer has a good mixing efficiency and an acceptable pressure drop; the Fins 35 degrees mixer is more efficient and compact, but requires a larger pressure drop. Their performance indicates that their use is possible on industrial applications.

Stable Model Predictive Control for Integrating Systems with Optimizing Targets

This paper concern the development of a stable model predictive controller (MPC) to be integrated with real time optimization (RTO) in the control structure of a process system with stable and integrating outputs. The real time process optimizer produces Optimal targets for the system inputs and for Outputs that Should be dynamically implemented by the MPC controller. This paper is based oil a previous work (Comput. Chem. Eng. 2005, 29, 1089) where a nominally stable MPC was proposed for systems with the conventional control approach where only the outputs have set points. This work is also based oil the work of Gonzalez et at. (J. Process Control 2009, 19, 110) where the zone control of stable systems is studied. The new control for is obtained by defining ail extended control objective that includes input targets and zone controller the outputs. Additional decision variables are also defined to increase the set of feasible solutions to the control problem. The hard constraints resulting from the cancellation of the integrating modes Lit the end of the control horizon are softened,, and the resulting control problem is made feasible to a large class of unknown disturbances and changes of the optimizing targets. The methods are illustrated with the simulated application of the proposed,approaches to a distillation column of the oil refining industry.

Photoinduced Degradation of the Herbicide Clomazone Model Reactions for Natural and Technical Systems

The photodegradation of the herbicide clomazone in the presence of S(2)O(8)(2-) or of humic substances of different origin was investigated. A value of (9.4 +/- 0.4) x 10(8) m(-1) s(-1) was measured for the bimolecular rate constant for the reaction of sulfate radicals with clomazone in flash-photolysis experiments. Steady state photolysis of peroxydisulfate, leading to the formation of the sulfate radicals, in the presence of clomazone was shown to be an efficient photodegradation method of the herbicide. This is a relevant result regarding the in situ chemical oxidation procedures involving peroxydisulfate as the oxidant. The main reaction products are 2-chlorobenzylalcohol and 2-chlorobenzaldehyde. The degradation kinetics of clomazone was also studied under steady state conditions induced by photolysis of Aldrich humic acid or a vermicompost extract (VCE). The results indicate that singlet oxygen is the main species responsible for clomazone degradation. The quantum yield of O(2)(a(1)Delta(g)) generation (lambda = 400 nm) for the VCE in D(2)O, Phi(Delta) = (1.3 +/- 0.1) x 10(-3), was determined by measuring the O(2)(a(1)Delta(g)) phosphorescence at 1270 nm. The value of the overall quenching constant of O(2)(a(1)Delta(g)) by clomazone was found to be (5.7 +/- 0.3) x 10(7) m(-1) s(-1) in D(2)O. The bimolecular rate constant for the reaction of clomazone with singlet oxygen was k(r) = (5.4 +/- 0.1) x 10(7) m(-1) s(-1), which means that the quenching process is mainly reactive.