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.

High Carbon Ferro-chromium Production by Self-reducing Process: Effects of Fe-Si and Fluxing Agent Additions

The technology of self-reducing pellets for ferro-alloys production is becoming an emerging process due to the lower electric energy consumption and the improvement of metal recovery in comparison with the traditional process. This paper presents the effects of reduction temperature, addition of ferro-silicon and addition of slag forming agents for the production of high carbon ferro-chromium by utilization of self-reducing pellets. These pellets were composed of Brazilian chromium ore (chromite) concentrate, petroleum coke, Portland cement, ferro-silicon and slag forming components (silica and hydrated lime). The pellets were processed at 1 773 K, 1 823 K and 1 873 K using an induction furnace. The products obtained, containing slag and metallic phases, were analyzed by scanning electron microscopy and chemical analyses (XEDS). A large effect on the reduction time was observed by increasing the temperature from 1 773 K to 1 823 K for pellets without Fe-Si addition: around 4 times faster at 1 823 K than at 1 773 K for reaction fraction close to one. However, when the temperature was further increased from 1 823 K to 1 873 K the kinetics improved by double. At 1 773 K, the addition of 2% of ferro-silicon in the pellet resulted in an increasing reaction rate of around 6 times, in comparison with agglomerate without it. The addition of fluxing agents (silica and lime), which form initial slag before the reduction is completed, impaired the full reduction. These pellets became less porous after the reduction process.

Abrasion and corrosion resistance of new Ni-based coating deposited by HVOF thermal spray process

Coatings based on NiCrAlC intermetallic based alloy were applied on AISI 316L stainless steel substrates using a high velocity oxygen fuel torch. The influence of the spray parameters on friction and abrasive wear resistance were investigated using an instrumented rubber wheel abrasion test, able to measure the friction forces. The corrosion behaviour of the coatings were studied with electrochemical techniques and compared with the corrosion resistance of the substrate material. Specimens prepared using lower O(2)/C(3)H(8) ratios showed smaller porosity values. The abrasion wear rate of the NiCrAlC coatings was much smaller than that described in the literature for bulk as cast materials with similar composition and one order of magnitude higher than bulk cast and heat treated (aged) NiCrAlC alloy. All coatings showed higher corrosion resistance than the AISI 316L substrate in HCl (5%) aqueous solution at 40 degrees C.

High Carbon Ferro-Chromium by Self-Reducing Process

This paper discusses the effects of temperature, addition of ferro-silicon and fluxing agents for the production of high carbon ferro-chromium by self-reducing process. The use of self-reducing agglomerates for ferro-alloys production is becoming an emerging processing technology due to lowering the electric energy consumption and improving the metal recovery in comparison with traditional ones. The self-reducing pellets were composed by chromite, petroleum coke, cement and small (0.1% - 2%) addition of ferro-silicon. The slag composition was adjusted by addition of fluxing agents. The reduction of pellets was carried out at 1773K (1500 degrees C), 1823K (1550 degrees C) and 1873K (1600 degrees C) by using induction furnace. The products obtained, containing slag and metallic phases, were analyzed by scanning electron microscopy and chemical analyses (XEDS). By increasing temperature from 1773K to 1823K large effect on the reduction time was observed. It decreased from 30 minutes to 10 minutes, for reaching around 0.98 reduction fraction. No significant effect on reduction time was observed when the reduction temperature was increased from 1823K to 1873K. At 1773K, the addition of 2% of ferro-silicon in the pellet resulted in an increasing reaction rate of around 6 times, in comparison with agglomerate without this addition. The addition of fluxing agents (silica and hydrated lime) has effect on reduction time (inverse relationship) and the pellets become less porous after reduction.

Development of a Continuous Emulsion Copolymerization Process in a Tubular Reactor

This contribution describes the development of a continuous emulsion copolymerization processs for vinyl acetate and n-butyl acrylate in a tubular reactor. Special features of this reactor include the use of oscillatory (pulsed) flow and internals (sieve plates) to prevent polymer fouling and promote good radial mixing, along with a controlled amount of axial mixing. The copolymer system studied (vinyl acetate and butyl acrylate) is strongly prone to composition drift due to very different reactivity ratios. An axially dispersed plug flow model, based on classical free radical copolymerization kinetics, was developed for this process and used successfully to optimize the lateral feeding profile to reduce compositional drift. An energy balance was included in the model equations to predict the effect of temperature variations on the process. The model predictions were validated with experimental data for monomer conversion, copolymer composition, average particle size, and temperature measured along the reactor length.

Modeling Vinyl Acetate and n-Butyl Acrylate Emulsion Copolymerization Process

This work presents a mathematical model for the vinyl acetate and n-butyl acrylate emulsion copolymerization process in batch reactors. The model is able to explain the effects of simultaneous changes in emulsifier concentration, initiator concentration, monomer-to-water ratio, and monomer feed composition on monomer conversion, copolymer composition and, to lesser extent, average particle size evolution histories. The main features of the system, such as the increase in the rate of polymerization as temperature, emulsifier, and initiator concentrations increase are correctly represented by the model. The model accounts for the basic features of the process and may be useful for practical applications, despite its simplicity and a reduced number of adjustable parameters.

MODELING ELECTRODIALYSIS AND A PHOTOCHEMICAL PROCESS FOR THEIR INTEGRATION IN SALINE WASTEWATER TREATMENT

Oxidation processes can be used to treat industrial wastewater containing non-biodegradable organic compounds. However, the presence of dissolved salts may inhibit or retard the treatment process. In this study, wastewater desalination by electrodialysis (ED) associated with an advanced oxidation process (photo-Fenton) was applied to an aqueous NaCl solution containing phenol. The influence of process variables on the demineralization factor was investigated for ED in pilot scale and a correlation was obtained between the phenol, salt and water fluxes with the driving force. The oxidation process was investigated in a laboratory batch reactor and a model based on artificial neural networks was developed by fitting the experimental data describing the reaction rate as a function of the input variables. With the experimental parameters of both processes, a dynamic model was developed for ED and a continuous model, using a plug flow reactor approach, for the oxidation process. Finally, the hybrid model simulation could validate different scenarios of the integrated system and can be used for process optimization.

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.

MPC for tracking zone regions

This paper deals with the problem of tracking target sets using a model predictive control (MPC) law. Some MPC applications require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some other variables - possibly including input variables - are steered to fixed target or set-point. In real applications, this problem is often overcome by including and excluding an appropriate penalization for the output errors in the control cost function. In this way, throughout the continuous operation of the process, the control system keeps switching from one controller to another, and even if a stabilizing control law is developed for each of the control configurations, switching among stable controllers not necessarily produces a stable closed loop system. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. In this work, a stable MPC formulation for constrained linear systems, with several practical properties is developed for this scenario. The concept of distance from a point to a set is exploited to propose an additional cost term, which ensures both, recursive feasibility and local optimality. The performance of the proposed strategy is illustrated by simulation of an ill-conditioned distillation column. (C) 2010 Elsevier Ltd. All rights reserved.

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.

Control charts for individual observations of a bivariate Poisson process

In this paper, three single-control charts are proposed to monitor individual observations of a bivariate Poisson process. The specified false-alarm risk, their control limits, and ARLs were determined to compare their performances for different types and sizes of shifts. In most of the cases, the single charts presented better performance rather than two separate control charts ( one for each quality characteristic). A numerical example illustrates the proposed control charts.

Diagnostic errors and repetitive sequential classifications in on-line process control by attributes

The procedure of on-line process control by attributes, known as Taguchi`s on-line process control, consists of inspecting the mth item (a single item) at every m produced items and deciding, at each inspection, whether the fraction of conforming items was reduced or not. If the inspected item is nonconforming, the production is stopped for adjustment. As the inspection system can be subject to diagnosis errors, one develops a probabilistic model that classifies repeatedly the examined item until a conforming or b non-conforming classification is observed. The first event that occurs (a conforming classifications or b non-conforming classifications) determines the final classification of the examined item. Proprieties of an ergodic Markov chain were used to get the expression of average cost of the system of control, which can be optimized by three parameters: the sampling interval of the inspections (m); the number of repeated conforming classifications (a); and the number of repeated non-conforming classifications (b). The optimum design is compared with two alternative approaches: the first one consists of a simple preventive policy. The production system is adjusted at every n produced items (no inspection is performed). The second classifies the examined item repeatedly r (fixed) times and considers it conforming if most classification results are conforming. Results indicate that the current proposal performs better than the procedure that fixes the number of repeated classifications and classifies the examined item as conforming if most classifications were conforming. On the other hand, the preventive policy can be averagely the most economical alternative rather than those ones that require inspection depending on the degree of errors and costs. A numerical example illustrates the proposed procedure. (C) 2009 Elsevier B. V. All rights reserved.

Effects of the preventive and corrective adjustments in economical designs for online process control for attributes with misclassification errors

The procedure for online process control by attributes consists of inspecting a single item at every m produced items. It is decided on the basis of the inspection result whether the process is in-control (the conforming fraction is stable) or out-of-control (the conforming fraction is decreased, for example). Most articles about online process control have cited the stoppage of the production process for an adjustment when the inspected item is non-conforming (then the production is restarted in-control, here denominated as corrective adjustment). Moreover, the articles related to this subject do not present semi-economical designs (which may yield high quantities of non-conforming items), as they do not include a policy of preventive adjustments (in such case no item is inspected), which can be more economical, mainly if the inspected item can be misclassified. In this article, the possibility of preventive or corrective adjustments in the process is decided at every m produced item. If a preventive adjustment is decided upon, then no item is inspected. On the contrary, the m-th item is inspected; if it conforms, the production goes on, otherwise, an adjustment takes place and the process restarts in-control. This approach is economically feasible for some practical situations and the parameters of the proposed procedure are determined minimizing an average cost function subject to some statistical restrictions (for example, to assure a minimal levelfixed in advanceof conforming items in the production process). Numerical examples illustrate the proposal.

Single step process for particles surface modification or thin film composite production

Adsorbent materials and composites are quite useful for sensor development. Therefore, the aim of this work is the surface modification of particulates and/or composite formation. The material was produced by plasma polymerization of HMDS (hexamethyldisilazane) in a single step. SEM analysis shows good surface coverage of particulates with a plasma polymerized film formed by several clusters that might increase adsorption. Particles (starch. 5 5 mu m) recovered with HMDS films show good properties for retention of medium-size Organic molecules, such as dye. Thin films formed by a mixture of particles and plasma polymerized thin film HMDS species were obtained in a single step and can be used for retention of organic compounds, in liquid or gaseous phase. (C) 2009 Elsevier B.V. All rights reserved.

Valve friction and nonlinear process model closed-loop identification

Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.