Lúcia Valéria Ramos de Arruda

A mixed integer programming approach for scheduling commodities in a pipeline

This paper addresses the problem of developing an optimisation structure to aid the operational decision-making of scheduling activities in a real-world pipeline scenario. The pipeline connects an inland refinery to a harbour, conveying different types of oil derivatives. The optimisation structure is developed based on mixed integer linear programming (MILP) with uniform time discretisation, but the MILP well-known computational burden is avoided by the proposed decomposition strategy, which relies on an auxiliary routine to determine temporal constraints, two MILP models, and a database. The scheduling of operational activities takes into account product availability, tankage constraints, pumping sequencing, flow rate determination, and a variety of operational requirements. The optimisation structure main task is to predict the pipeline operation during a limited scheduling horizon, providing low cost operational procedures. Illustrative instances demonstrate that the optimisation structure is able to define new operational points to the pipeline system, providing significant cost saving.

Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps

This study developed an autonomous navigation system using Fuzzy Cognitive Maps (FCM). Fuzzy Cognitive Map is a tool that can model qualitative knowledge in a structured way through concepts and causal relationships. Its mathematical representation is based on graph theory. A new variant of FCM, named Event Driven-Fuzzy Cognitive Maps (ED-FCM), is proposed to model decision tasks and/or make inferences in autonomous navigation. The FCM’s arcs are updated from the occurrence of special events as dynamic obstacle detection. As a result, the developed model is able to represent the robot’s dynamic behavior in presence of environment changes. This model skill is achieved by adapting the FCM relationships among concepts. A reinforcement learning algorithm is also used to finely adjust the robot behavior. Some simulation results are discussed highlighting the ability of the autonomous robot to navigate among obstacles (navigation at unknown environment). A fuzzy based navigation system is used as a reference to evaluate the proposed autonomous navigation system performance.

Startup of a distillation column using intelligent control techniques

Abstract This work proposes the development of an intelligent predictive controller. Recurrent neural networks are used to identify the process, providing predictions about its behavior, based on control actions applied to the system. These information are then used by fuzzy controllers to accomplish a better control performance. Moreover, the fuzzy controller membership functions are evolved by Genetic algorithms (GAs) allowing an automatic tune of controllers. The combined use of these techniques make possible the control of multi-variable processes using several fuzzy controllers where the coupling among controlled variables are modeled by neural networks, and control objectives can be inserted into the GA fitness function. The methodology was applied to a simulation of the startup of a continuous distillation column. This process is chosen due to their characteristics, such as inertia, large accommodation time and conflicting control objectives that make these processes hard to control with traditional methods.

A combined CLP-MILP approach for scheduling commodities in a pipeline

This paper addresses the problem of developing an optimization model to aid the operational scheduling in a real-world pipeline scenario. The pipeline connects refinery and harbor, conveying different types of commodities (gasoline, diesel, kerosene, etc.). An optimization model was developed to determine pipeline scheduling with improved efficiency. This model combines constraint logic programming (CLP) and mixed integer linear programming (MILP) in a CLP-MILP approach. The proposed model uses decomposition strategies, continuous time representation, intervals that indicate time constraints (time windows), and a series of operational issues, such as the seasonal and hourly cost of electric energy (on-peak demand hours). Real cases were solved in a matter of seconds. The computational results have demonstrated that the model is able to define new operational points to the pipeline, providing significant cost savings. Indeed the CLP-MILP model is an efficient tool to aid operational decision-making within this real-world pipeline scenario.

Simulating the operational scheduling of a realworld pipeline network

Abstract This paper addresses the problem of developing a simulation model to aid the operational decision-making of scheduling activities in a real-world pipeline network. Basically, the simulation model should represent three different behaviors: production, transport and demand of oil derivatives. Batches are pumped from (or pass through) many different areas and flow through pipes which are the shared resources at the network. It is considered that different products can flow through the same pipe and each oil derivative has its proper tankfarm at refineries, terminals or harbor. The simulator makes use of an optimal scheduling sequence of batches that balance demand requirements to the production planning, considering inventory management issues and pipeline pumping procedures. The simulation model represents a real-world pipeline network designed to aid typical activities of an operator such as inventory management at different and batch performance analysis by visualization tank levels and pipe utilization rate.

A dynamic fuzzy cognitive map applied to chemical process supervision

This work develops an intelligent tool based on fuzzy cognitive maps to supervisory process control. Fuzzy cognitive maps are a neuro-fuzzy methodology that can accurate model complexly system using a causal-effect fuzzy reasoning. In the proposed approach, new types of concept and relation, not restricted to cause-effect ones, are added to the model resulting in a dynamic fuzzy cognitive map (D-FCM). In this sense, a supervisory system is developed in order to control a fermentation process. This process has a non-linear behavior and presents several problems, such as non-minimum phase and large accommodation time. The supervisor goal is to operate the process in normal and critical conditions. The expert knowledge about the process behavior in both conditions is used to build the D-FCM supervisor. Simulation results are presented in order to validate the proposed intelligent supervisor.

A neuro-coevolutionary genetic fuzzy system to design soft sensors

This paper addresses a soft computing-based approach to design soft sensors for industrial applications. The goal is to identify second-order Takagi–Sugeno–Kang fuzzy models from available input/output data by means of a coevolutionary genetic algorithm and a neuro-based technique. The proposed approach does not require any prior knowledge on the data-base and rule-base structures. The soft sensor design is carried out in two steps. First, the input variables of the fuzzy model are pre-selected from the secondary variables of a dynamical process by means of correlation coefficients, Kohonen maps and Lipschitz quotients. Such selection procedure considers nonlinear relations among the input and output variables. Second, a hierarchical coevolutionary methodology is used to identify the fuzzy model itself. Membership functions, individual rules, rule-bases and fuzzy inference parameters are encoded into each hierarchical level and a shared fitness evaluation scheme is used to measure the performance of individuals in such levels. The proposed methodology is evaluated by developing soft sensors to infer the product composition in petroleum refining processes. The obtained results are compared with other benchmark approaches, and some conclusions are presented.

PID control of MIMO process based on rank niching genetic algorithm

Abstract Non-linear multiple-input multiple-output (MIMO) processes which are common in industrial plants are characterized by significant interactions and non- linearities among their variables. Thus, tuning several controllers in complex industrial plants is a challenge for process engineers and operators. An approach for adjusting the parameters of n proportional–integral–derivative (PID) controllers based on multiobjective optimization and genetic algorithms (GA) is presented in this paper. A modified genetic algorithm with elitist model and niching method is developed to guarantee a set of solutions (set of PID parameters) with different tradeoffs regarding the multiple requirements of the control performance. Experiments considering a fluid catalytic cracking (FCC) unit, under PI and dynamic matrix control (DMC) are carried out in order to evaluate the proposed method. The results show that the proposed approach is an alternative to classical techniques as Ziegler–Nichols rules and others.

An object detection and recognition system for weld bead extraction from digital radiographs

With base in object detection and recognition techniques, we developed and implemented a new methodology to perform the first head-function of a weld quality interpretation system: the weld bead extraction from a digital radiograph. The proposed methodology uses a genetic algorithm to manage the search for suitable parameters values (position, width, length, and angle) that best defines a window, in the radiographic image, matching with the model image of a weld bead sample. The search results are verified in a classification process that recognize true detections using image matching parameters also proposed in this work. To test the proposed methodology, two groups of images were used; one consisting of 110 radiographs from pipelines welded joints and the other containing 6 images with different numbers of radiographs per image. The tests results showed that, besides automatically check the number of weld beads per image, the proposed methodology is also able to supply the respective position, width, length, and angle of each weld bead, with an accurate rate of 94.4%. As a result, the detected weld beads are correctly extracted from the original image and made available to be inspected through others algorithms for failure detection and classification.

Robust estimation of parametric membership regions

This paper is concerned with the robust identification of linear models when modelling error is bounded. A modified Hopfields neural network is used to calculate a membership set for the model parameters, with the internal parameters of the network obtained using the valid-subspace technique. These parameters can be explicitly computed to guarantee the network convergence. A solution for the robust estimation problem with an unknown-but-bounded error corresponds to an equilibrium point of the network. A comparative analysis with alternative robust estimation methods is provided to illustrate the proposed approach.