Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Distributed control of drinking water networks using population dynamics: Barcelona case study

Trabajo presentado a la 53rd IEEE Conference on Decision and Control (CDC 2014), celebrada del 15 al 17 de diciembre en Los Angeles, California (US).Large-scale network systems involve a large number of variables, making the design of real-time controllers challenging. A distributed controller design allows to reduce computational requirements since tasks may be divided into different subsystems, making possible to guarantee real-time processing. This paper proposes a constrained optimization based distributed control design by applying a novel population and masses dynamics approach. The distributed controller design is applied to the Barcelona Drinking Water Network (DWN) in order to illustrate its effectiveness in the control performance.This work has been partially supported by the projects COLCIENCIAS 548/2012, ECOCIS (Ref. DPI2013-48243-C2-1-R) and EFFINET (Ref. FP7-ICT-2011-8-31855). J. Barreiro-Gómez is supported by COLCIENCIAS and by the Agència de Gestió d’Ajust Universitaris i de Recerca AGAUR.Peer Reviewe

Modeling, diagnosis, and control of fuel-cell-based technologies and their integration in smart grids and automotive systems

Society is gradually becoming aware that the current energy system based on the use of fossil fuels is inefficient, highly polluting, and finite supply. Within the scientific community and industry stakeholders, there is a unified agreement that indicates that hydrogen (H2), as an energy vector, combined with other sources of alternative energy, represents a safe and viable option to mitigate the problems associated with hydrocarbon combustion because the entire system can be developed as an efficient, clean, and sustainable energy source. In this context, the change from the current energy system to a new system with a stronger involvement of H2 relentlessly involves the introduction of fuel cells as elements of efficient energy conversion.Peer Reviewe

Distributed MPC with time-varying communication network: A density-dependent population games approach

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work addresses distributed control design by using density-dependent population dynamics. Furthermore, stability of the equilibrium point under this proposed class of population dynamics is studied, and the relationship between the equilibrium point of density-dependent population games (DDPG) and the solution of constrained optimization problems is shown. Finally, a distributed predictive control is designed with the proposed density-dependent dynamics, and contemplating a time-varying communication network.Peer ReviewedPostprint (author's final draft

Algebraic observer design for PEM fuel cell system

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, the concept of the algebraic observer is applied to Proton Exchange Membrane Fuel Cell (PEMFC) system. The aim of the proposed observer is to reconstruct the oxygen excess ratio through estimation of their relevant states in real time from the measurement of the supply manifold air pressure. A robust differentiation method is adopted to estimate in finite-time the time derivative of the supply manifold air pressure. Then, the relevant states are reconstructed based on the output-state inversion model. The objective is to minimize the use of extra sensors in order to reduce the costs and enhance the system accuracy. The performance of the proposed observer is analyzed through simulations considering measurement noise and different stack-current variations. The results show that the algebraic observer estimates in finite time and robustly the oxygen-excess ratio.Peer ReviewedPostprint (author's final draft

Model predictive control based on LPV models with parameter-varying delays

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a Model Predictive Control (MPC) strategy based on Linear Parameter Varying (LPV) models with varying delays affecting states and inputs. The proposed control approach allows the controller to accommodate the scheduling parameters and delay change. By computing the prediction of the state variables and delay along a prediction time horizon, the system model can be modified according to the evaluation of the estimated state and delay at each time instant. Moreover, the solution of the optimization problem associated with the MPC design is achieved by solving a series of Quadratic Programming (QP) problem at each time instant. This iterative approach reduces the computational burden compared to the solution of a non-linear optimization problem. A pasteurization plant system is used as a case study to demonstrate the effectiveness of the proposed approach.Peer ReviewedPostprint (author's final draft

Time-varying partitioning for predictive control design: density-games approach

The design of distributed optimization-based controllers for large-scale systems (LSSs) implies every time new challenges. The fact that LSSs are generally located throughout large geographical areas makes dicult the recollection of measurements and their transmission. In this regard, the communication network that is required for a centralized control approach might have high associated economic costs. Furthermore, the computation of a large amount of data implies a high computational burden to manage, process and use them in order to make decisions over the system operation. A plausible solution to mitigate the aforementioned issues associated with the control of LSSs consists in dividing this type of systems into smaller sub-systems able to be handled by independent local controllers. This paper studies two fundamental components of the design of distributed optimization-based controllers for LSSs, i.e., the system partitioning and distributed optimization algorithms. The design of distributed model predictive control (DMPC) strategies with a system partitioning and by using density-dependent population games (DDPG) is presented.Peer ReviewedPostprint (author's final draft

Decentralised fault diagnosis of large-scale systems: Application to water transport networks

Trabajo presentado al 26th International Workshop on Principles of Diagnosis celebrado en Paris (Francia) del 31 de agosto al 3 de septiembre de 2015.In this paper, a decentralised fault diagnosis approach for large-scale systems is proposed. This approach is based on obtaining a set of local diagnosers using the analytical redundancy relation (ARRs) approach. The proposed approach starts with obtaining the set of ARRs of the system yielding into an equivalent graph. From that graph, the graph partitioning problem is solved obtaining a set of ARRs for each local diagnoser. Finally, a decentralised fault diagnosis strategy is proposed and applied over the resultant set of partitions and ARRs. In order to illustrate the application of the proposed approach, a case study based on the Barcelona drinking water network (DWN) is used.This work has been partially supported by the EFFINET grant FP7-ICT-2012-318556 of the European Commission and the Spanish project ECOCIS (Ref. DPI2013-48243-C2-1-R).Peer Reviewe

Output-feedback model predictive control of a pasteurization pilot plant based on an LPV model

In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.Peer ReviewedPostprint (author's final draft

Reliability-based economic model predictive control for generalized flow-based networks including actuators' health-aware capabilities

This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalized flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamically allocate safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the considered case study.Peer ReviewedPostprint (author's final draft