Short-term experimental data of drying shrinkage of ground granulated blast-furnace slag cement concrete

This paper presents results of laboratory testing of unrestrained drying shrinkage during a period of 154 days of different concrete mixtures from the Brazilian production line that utilize ground granulated blast-furnace slag in their compositions. Three concrete mixtures with water/cement ratio of 0.78(M1), 0.41(M2), and 0.37(M3) were studied. The obtained experimental data were compared with the analytical results from prediction models available in the literature: the ACI 209 model (ACI), the B3 model (B3), the Eurocode 2 model (EC2), the GL 2000 model (GL), and the Brazilian NBR 6118 model (NBR), and an analysis of the efficacy of these models was conducted utilizing these experimental data. In addition, the development of the mechanical properties (compressive strength and modulus of elasticity) of the studied concrete mixtures was also measured in the laboratory until 126 days. From this study, it could be concluded that the ACI and the GL were the models that most approximated the experimental drying shrinkage data measured during the analyzed period of time.

Fast experimental identification of suspension models for control design

This paper presents both the theoretical and the experimental approaches of the development of a mathematical model to be used in multi-variable control system designs of an active suspension for a sport utility vehicle (SUV), in this case a light pickup truck. A complete seven-degree-of-freedom model is successfully quickly identified, with very satisfactory results in simulations and in real experiments conducted with the pickup truth. The novelty of the proposed methodology is the use of commercial software in the early stages of the identification to speed up the process and to minimize the need for a large number of costly experiments. The paper also presents major contributions to the identification of uncertainties in vehicle suspension models and in the development of identification methods using the sequential quadratic programming, where an innovation regarding the calculation of the objective function is proposed and implemented. Results from simulations of and practical experiments with the real SUV are presented, analysed, and compared, showing the potential of the method.

Identification and control of occupational risks in a Sao Paulo metropolitan region quarry

Crushed stone mining is the third largest mining economy in Brazil, where almost half is produced in the Sao Paulo metropolitan region. The segment registers the highest number of accidents among the extractive industries, which justifies the concern with workers` health and safety, and the importance of controlling occupational hazards. Since 2002, the NR-22 Standard (NR-22: Occupational Health and Safety in Mining) makes compulsory the elaboration of a Risk Management Program that identifies risks and establishes control measures. Considering the crushed stone mining industry importance to the state, this paper evaluates and discusses the risks identified in unit operations during the production process of crushed stone in an open pit mine in order to propose control measures for the development of the Risk Management Program. Although this study refers to a specific quarry, it can be applied to other mines from the same sector since some considerations are made regarding differences in manufacturing processes. The research was based on the identification of the main risks associated with drilling, blasting, load & haulage, crushing and screening through field measurements of some hazardous agents, together with company reports. The results contributed to the choice of the appropriate control measures for the improvement Of workers` health and safety conditions.

A simple and effective inverse projection scheme for void distribution control in topology optimization

The ability to control both the minimum size of holes and the minimum size of structural members are essential requirements in the topology optimization design process for manufacturing. This paper addresses both requirements by means of a unified approach involving mesh-independent projection techniques. An inverse projection is developed to control the minimum hole size while a standard direct projection scheme is used to control the minimum length of structural members. In addition, a heuristic scheme combining both contrasting requirements simultaneously is discussed. Two topology optimization implementations are contributed: one in which the projection (either inverse or direct) is used at each iteration; and the other in which a two-phase scheme is explored. In the first phase, the compliance minimization is carried out without any projection until convergence. In the second phase, the chosen projection scheme is applied iteratively until a solution is obtained while satisfying either the minimum member size or minimum hole size. Examples demonstrate the various features of the projection-based techniques presented.

Modeling, control and experimental validation of a novel actuator based on shape memory alloys

The paper presents the development of a mechanical actuator using a shape memory alloy with a cooling system based on the thermoelectric effect (Seebeck-Peltier effect). Such a method has the advantage of reduced weight and requires a simpler control strategy as compared to other forced cooling systems. A complete mathematical model of the actuator was derived, and an experimental prototype was implemented. Several experiments are used to validate the model and to identify all parameters. A robust and nonlinear controller, based on sliding-mode theory, was derived and implemented. Experiments were used to evaluate the actuator closed-loop performance, stability, and robustness properties. The results showed that the proposed cooling system and controller are able to improve the dynamic response of the actuator. (C) 2009 Elsevier Ltd. All rights reserved.

Development of Control Systems for Safety Instrumented Systems

Safety Instrumented Systems (SIS) are designed to prevent and / or mitigate accidents, avoiding undesirable high potential risk scenarios, assuring protection of people`s health, protecting the environment and saving costs of industrial equipment. The design of these systems require formal methods for ensuring the safety requirements, but according material published in this area, has not identified a consolidated procedure to match the task. This sense, this article introduces a formal method for diagnosis and treatment of critical faults based on Bayesian network (BN) and Petri net (PN). This approach considers diagnosis and treatment for each safety instrumented function (SIF) including hazard and operability (HAZOP) study in the equipment or system under control. It also uses BN and Behavioral Petri net (BPN) for diagnoses and decision-making and the PN for the synthesis, modeling and control to be implemented by Safety Programmable Logic Controller (PLC). An application example considering the diagnosis and treatment of critical faults is presented and illustrates the methodology proposed.

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.

Fretting fatigue in overhead conductors: Rig design and failure analysis of a Grosbeak aluminium cable steel reinforced conductor

The performance optimisation of overhead conductors depends on the systematic investigation of the fretting fatigue mechanisms in the conductor/clamping system. As a consequence, a fretting fatigue rig was designed and a limited range of fatigue tests was carried out at the middle high cycle fatigue regime in order to access an exploratory S-N curve for a Grosbeak conductor, which was mounted on a mono-articulated aluminium clamping system. Subsequent to these preliminary fatigue tests, the components of the conductor/clamping system, such as ACSR conductor, upper and lower clamps, bolt and nuts, were subjected to a failure analysis procedure in order to investigate the metallurgical free variables interfering on the fatigue test results, aiming at the optimisation of the testing reproducibility. The results indicated that the rupture of the planar fracture surfaces observed in the external At strands of the conductor tested under lower bending amplitude (0.9 mm) occurred by fatigue cracking (I mm deep), followed by shear overload. The V-type fracture surfaces observed in some At strands of the conductor tested under higher bending amplitude (1.3 mm) were also produced by fatigue cracking (approximately 400 mu m deep), followed by shear overload. Shear overload fracture (45 degrees fracture surface) was also observed on the remaining At wires of the conductor tested under higher bending amplitude (1.3 mm). Additionally, the upper and lower Al-cast clamps presented microstructure-sensitive cracking, which was folowed by particle detachment and formation of abrasive debris on the clamp/conductor tribo-interface, promoting even further the fretting mechanism. The detrimental formation of abrasive debris might be inhibited by the selection of a more suitable class of as-cast At alloy for the production of clamps. Finally, the bolt/nut system showed intense degradation of the carbon steel nut (fabricated in ferritic-pearlitic carbon steel, featuring machined threads with 190 HV), with intense plastic deformation and loss of material. Proper selection of both the bolt and nut materials and the finishing processing might prevent the loss in the clamping pressure during the fretting testing. It is important to control the specification of these components (clamps, bolt and nuts) prior to the start of large scale fretting fatigue testing of the overhead conductors in order to increase the reproducibility of this assessment. (c) 2008 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.

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.

Robust model predictive control with zone control

Model predictive control (MPC) is usually implemented as a control strategy where the system outputs are controlled within specified zones, instead of fixed set points. One strategy to implement the zone control is by means of the selection of different weights for the output error in the control cost function. A disadvantage of this approach is that closed-loop stability cannot be guaranteed, as a different linear controller may be activated at each time step. A way to implement a stable zone control is by means of the use of an infinite horizon cost in which the set point is an additional variable of the control problem. In this case, the set point is restricted to remain inside the output zone and an appropriate output slack variable is included in the optimisation problem to assure the recursive feasibility of the control optimisation problem. Following this approach, a robust MPC is developed for the case of multi-model uncertainty of open-loop stable systems. The controller is devoted to maintain the outputs within their corresponding feasible zone, while reaching the desired optimal input target. Simulation of a process of the oil re. ning industry illustrates the performance of the proposed strategy.

A stable MPC with zone control

Several MPC applications implement a control strategy in which some of the system outputs are controlled within specified ranges or zones, rather than at fixed set points [J.M. Maciejowski, Predictive Control with Constraints, Prentice Hall, New Jersey, 2002]. This means that these outputs will be treated as controlled variables only when the predicted future values lie outside the boundary of their corresponding zones. The zone control is usually implemented by selecting an appropriate weighting matrix for the output error in the control cost function. When an output prediction is inside its zone, the corresponding weight is zeroed, so that the controller ignores this output. When the output prediction lies outside the zone, the error weight is made equal to a specified value and the distance between the output prediction and the boundary of the zone is minimized. The main problem of this approach, as long as stability of the closed loop is concerned, is that each time an output is switched from the status of non-controlled to the status of controlled, or vice versa, a different linear controller is activated. Thus, throughout the continuous operation of the process, the control system keeps switching from one controller to another. 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. Here, a stable M PC is developed for the zone control of open-loop stable systems. Focusing on the practical application of the proposed controller, it is assumed that in the control structure of the process system there is an upper optimization layer that defines optimal targets to the system inputs. The performance of the proposed strategy is illustrated by simulation of a subsystem of an industrial FCC system. (C) 2008 Elsevier Ltd. All rights reserved.

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.