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

Dynamic hybrid simulation of batch processes driven by a scheduling module

Simulation is now a CAPE tool widely used by practicing engineers for process design and control. In particular, it allows various offline analyses to improve system performance such as productivity, energy efficiency, waste reduction, etc. In this framework, we have developed the dynamic hybrid simulation environment PrODHyS whose particularity is to provide general and reusable object-oriented components dedicated to the modeling of devices and operations found in chemical processes. Unlike continuous processes, the dynamic simulation of batch processes requires the execution of control recipes to achieve a set of production orders. For these reasons, PrODHyS is coupled to a scheduling module (ProSched) based on a MILP mathematical model in order to initialize various operational parameters and to ensure a proper completion of the simulation. This paper focuses on the procedure used to generate the simulation model corresponding to the realization of a scenario described through a particular scheduling

Model checking embedded system designs

We survey the basic principles behind the application of model checking to controller verification and synthesis. A promising development is the area of guided model checking, in which the state space search strategy of the model checking algorithm can be influenced to visit more interesting sets of states first. In particular, we discuss how model checking can be combined with heuristic cost functions to guide search strategies. Finally, we list a number of current research developments, especially in the area of reachability analysis for optimal control and related issues

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

On electrical load tracking scheduling for a steel plant

Nolde and Morari (2010) study a steel manufacturing scheduling problem where the tasks must be scheduled such that electricity consumption matches to a pre-specified periodic energy chart. They propose a continuous time integer linear programming formulation to solve the problem. In this note, we present an alternative continuous time formulation, focused on the relative positions of tasks and time periods, that improves significantly the computation time

The earlier the better: a theory of timed actor interfaces

Programming embedded and cyber-physical systems requires attention not only to functional behavior and correctness, but also to non-functional aspects and specifically timing and performance. A structured, compositional, model-based approach based on stepwise refinement and abstraction techniques can support the development process, increase its quality and reduce development time through automation of synthesis, analysis or verification. Toward this, we introduce a theory of timed actors whose notion of refinement is based on the principle of worst-case design that permeates the world of performance-critical systems. This is in contrast with the classical behavioral and functional refinements based on restricting sets of behaviors. Our refinement allows time-deterministic abstractions to be made of time-non-deterministic systems, improving efficiency and reducing complexity of formal analysis. We show how our theory relates to, and can be used to reconcile existing time and performance models and their established theories