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Short-term production optimization of oil assets concerns routing decisions and equipment control settings that induce optimal steady-state operations. Typically, the decisions are made on the scale of hours to days with the aim of maximizing short-term economic and oil gains. Still an evolving field, this paper contributes by presenting a model and solution strategy for sequencing operations over time to drive the system towards an optimal state, while accounting for constraints that ensure smooth transitions until settling at the terminal steady-state. The constraints include the maximum number of wells that undergo a change in the control settings, maximum number and variation on the input signals for each well, and maximum number of changes in each time period, among others. Additionally, the proposed strategy can optimize the rate of injection of demulsifiers into wells considering the limits and impacts on fluid processing capacity. The paper reports simulated results for a real-world offshore production platform, demonstrating the effectiveness of the optimization strategy to cope with the failure and recovery of compression capacity, and to manage the injection of demulsifiers. The experiments show a gain of 11.5% in total oil production resulting from an optimal management of demulsifiers.

The deployment of offshore platforms for the extraction of oil and gas from subsea reservoirs presents unique challenges, particularly when multiple platforms are connected by a subsea gas network. In the Santos basin, the aim is to maximize oil production while maintaining safe and sustainable levels of CO2 content and pressure in the gas stream. To address these challenges, a novel methodology has been proposed that uses boundary conditions to coordinate the use of shared resources among the platforms. This approach decouples the optimization of oil production in platforms from the coordination of shared resources, allowing for more efficient and effective operation of the offshore oilfield. In addition to this methodology, a fast and accurate proxy model has been developed for gas pipeline networks. This model allows for efficient optimization of the gas flow through the network, taking into account the physical and operational constraints of the system. In experiments, the use of the proposed proxy model in tandem with derivative-free optimization algorithms resulted in an average error of less than 5% in pressure calculations, and a processing time that was over up to 1000 times faster than the phenomenological simulator. These results demonstrate the effectiveness and efficiency of the proposed methodology in optimizing oil production in offshore platforms connected by a subsea gas network, while maintaining safe and sustainable levels of CO2 content and pressure in the gas stream.

The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The time series data are from a sensor attached to an electrical machine (motor) measuring vibration variations in three axes: X (axial), Y (radial), and Z (radial X). The dataset is used to train a hybrid convolutional neural network with long short-term memory (CNN-LSTM) architecture. By employing quantile regression at the network output, the proposed approach aims to manage the uncertainties present in the data. The application of the hybrid CNN-LSTM attention-based model, combined with the use of quantile regression to capture uncertainties, yielded superior results compared to traditional reference models. These results can benefit companies by optimizing their maintenance schedules and improving the overall performance of their electric machines.

When managing a constellation of nanosatellites, one may leverage this structure to improve the mission’s quality-of-service (QoS) by optimally distributing the tasks during an orbit. In this sense, this research proposes an offline energy-aware task scheduling problem formulation regarding the specifics of constellations, by considering whether the tasks are individual, collective, or stimulated to be redundant. By providing such an optimization framework, the idea of estimating an offline task schedule can serve as a baseline for the constellation design phase. For example, given a particular orbit, from the simulation of an irradiance model, the engineer can estimate how the mission value is affected by the inclusion or exclusion of individuals objects. The proposed model, given in the form of a multi-objective mixed-integer linear programming model, is illustrated in this work for several illustrative scenarios considering different sets of tasks and constellations. We also perform an analysis of the Pareto-optimal frontier of the problem, identifying the feasible trade-off points between constellation and individual tasks. This information can be useful to the decision-maker (mission operator) when planning the behavior in orbit.

The interconnection of dynamic subsystems that share limited resources are found in many applications, and the control of such systems of subsystems has fueled significant attention from scientists and engineers. For the operation of such systems, model predictive control (MPC) has become a popular technique, arguably for its ability to deal with complex dynamics and system constraints. The MPC algorithms found in the literature are mostly centralized, with a single controller receiving the signals and performing the computations of output signals. However, the distributed structure of such interconnected subsystems is not necessarily explored by standard MPC. To this end, this work proposes hierarchical decomposition to split the computations between a master problem (centralized component) and a set of decoupled subproblems (distributed components) with activation constraints, which brings about organizational flexibility and distributed computation. Two general methods are considered for hierarchical control and optimization, namely Benders decomposition and outer approximation. Results are reported from a numerical analysis of the decompositions and a simulated application to energy management, in which a limited source of energy is distributed among batteries of electric vehicles.

Resumo: Apresenta-se um método de cálculo de frações de verde a ser usado em sistemas de controle de tráfego em tempo real por amostragem cíclica. A base do método origina-se da estratégia TUC, brevemente revisada neste trabalho. O objetivo é substituir um procedimento empírico de ajuste de parâmetros auxiliares do controle por uma metodologia que seja facilmente entendida e utilizável na prática. Resultados de simulação indicam o desempenho superior do método quando comparado com os ajustes empíricos do método original.Palavras-chave: controle semafório em tempo real; amostragem cíclica; método TUC.Abstract: A new method for the computation of splits for real-time traffic control with cyclic sampling is presented. The method is based on the TUC strategy, which is briefly reviewed in this paper. The goal is to replace an empirical tuning procedure of auxiliary control parameters by a method that is easy to understand and implement. Simulation results indicate the superior performance of the method when compared to the empirical adjustments of the original method.Keywords: real-time traffic control; cyclic sampling; TUC method.

Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network’s scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed.

Este trabalho apresenta o modelo e estratégia de controle de um sistema BRT (Bus Rapid Transit) que tem como principal característica a presença de segmento(s) de faixa exclusiva única para ambos os sentidos de deslocamento dos ônibus (segmentos bidirecionais). Este diferencial, dadas as restrições associadas ao alto grau de adensamento das áreas urbanas, permite uma menor ocupação de espaço físico e menor ocupação das vias convencionais por parte dos ônibus, podendo muitas vezes representar a viabilidade ou não da implementação de sistemas BRT. São apresentados o modelo e uma estratégia de controle em tempo real para operação do sistema BRT proposto, aqui denominado CIRSO (Controle Integrado de Retenção e Sequenciamento dos Ônibus). O modelo matemático utilizado é determinístico, representado por uma função custo e restrições, sendo as variáveis de controle o tempo ótimo de retenção dos ônibus nos pontos e o controle do sequenciamento dos ônibus através do(s) segmento(s) bidirecional(ais). Resultados de simulação mostram a viabilidade e eficiência do modelo do sistema BRT e da estratégia de controle CIRSO aqui propostos. Palavras-chave: faixa exclusiva para ônibus, faixa bidirecional para ônibus, BRT, controle do espaçamento Abstract: This paper presents a model and control strategy for a BRT (Bus Rapid Transit) system which has as its main distinguishing characteristic the presence of segments with only one exclusive lane for both directions of bus movement (bidirectional segments). This feature, given the constraints associated with high density urban areas, allows for a smaller physical footprint and lower occupancy of the conventional ways by buses and can often represent the only feasible alternative of the implementation of a BRT system. The model and a control strategy for real time operation of the proposed BRT system are presented, here called CIRSO (Integrated Control of Retention and Sequencing of the Buses). The mathematical model used is deterministic, represented by a cost function and constraints. The control variables are the optimum holding time of buses at stops and control of bus sequencing through the bidirectional segments. Simulation results show the feasibility and efficiency of the BRT system model and CIRSO control strategy here proposed. Keywords: exclusive bus way, bidirectional bus lane, bus rapid transit, holding control