Plattform zur Simulation von energetischen Einflussfaktoren in Matrix-Produktionssystemen

Matrix production systems are designed to be flexible and productive at the same time. This is to be achieved by a modular design and high degree of automation in terms of process control, material transport and work distribution. This also affects the flow of energy which results in a highly variable energetic behaviour of the overall system. This contribution presents a synthetic simulation platform approach to investigate the energetic behaviour of matrix production systems. The setup and modules of the approach are pointed out based on the typical characteristics of matrix production systems. An experiment study is showcased to demonstrate the approach and give an insight into the results of the simulation

Simulation Modeling for Energy-Flexible Manufacturing: Pitfalls and How to Avoid Them

Due to the high share of industry in total electricity consumption, industrial demand-side management can make a relevant contribution to the stability of power systems. At the same time, companies get the opportunity to reduce their electricity procurement costs by taking advantage of increasingly fluctuating prices on short-term electricity markets, the provision of system services on balancing power markets, or by increasing the share of their own consumption from on-site generated renewable energy. Demand-side management requires the ability to react flexibly to the power supply situation without negatively affecting production targets. It also means that the management and operation of production must consider not only production-related parameters but also parameters of energy availability, which further increase the complexity of decision-making. Although simulation studies are a recognized tool for supporting decision-making processes in production and logistics, the simultaneous simulation of material and energy flows has so far been limited mainly to issues of energy efficiency as opposed to energy flexibility, where application-oriented experience is still limited. We assume that the consideration of energy flexibility in the simulation of manufacturing systems will amplify already known pitfalls in conducting simulation studies. Based on five representative industrial use cases, this article provides practitioners with application-oriented experiences of the coupling of energy and material flows in simulation modeling of energy-flexible manufacturing, identifies challenges in the simulation of energy-flexible production systems, and proposes approaches to face these challenges. Seven pitfalls that pose a particular challenge in simulating energy-flexible manufacturing have been identified, and possible solutions and measures for avoiding them are shown. It has been found that, among other things, consistent management of all parties involved, early clarification of energy-related, logistical, and resulting technical requirements for models and software, as well as the application of suitable methods for validation and verification are central to avoiding these pitfalls. The identification and characterization of challenges and the derivation of recommendations for coping with them can raise awareness of typical pitfalls. This paper thus helps to ensure that simulation studies of energy-flexible production systems can be carried out more efficiently in the future

Control-oriented dynamical modelling and state estimation of centrifugal fans with induction motors drives

To achieve significant energy savings in air and water supply systems, variable frequency induction motor drives are employed to power centrifugal fans and pumps. The cost of implementing closed loop control for energy savings can be reduced by replacing expensive flow rate or pressure sensors with estimators that are based on monitored variables from the drives and are embedded into their software. Existing estimation techniques rely on quasi-steady modelling of fans and pumps using data from steady state experiments or data sheets. However, this approach faces challenges during transients, which are addressed in the present thesis. This research proposes quasi-steady and dynamical estimators for the flow rate and pressure of a centrifugal fan with an induction motor drive, based on neural networks trained using both steady state and transient experimental data. The thesis describes a test rig designed specifically for this purpose, which utilizes an industrial centrifugal fan from Nicotra- Gebhardt equipped with a three-phase induction motor. Additionally, the thesis presents a detailed procedure for designing the estimators. The use of electrical drives for controlling centrifugal fans and pumps is a wellestablished practice that can lead to significant energy savings. However, this requires electrical and automation engineers to possess knowledge relevant to the modelling of fans and pumps in relation to drives. Existing approaches rely heavily on quasisteady modelling, which is widely used by drives application experts, but there is limited adoption of dynamical modelling that is integrated with AC drives. This thesis aims to enhance existing dynamical models by employing neural network estimation to calculate the overall efficiency of the fan/pump. The model separates the fan/pump's own efficiency for the computation of motor load torque to reflect accurately the power balance. The developed model is designed to be suitable for control design applications. Additionally, this research verifies the dynamical model experimentally, which supports the necessity of incorporating a first-order nonlinear differential equation to model the flow rate dynamics. A linearized mathematical model of a centrifugal fan powered by a Squirrel Cage Induction Motor has been developed and validated through experimental and simulation data. Furthermore, a H mixed sensitivity approach has been employed to design a controller for a system that aims to achieve performance objectives such as stability, disturbance rejection, and reference tracking. The proposed H mixed sensitivity controller meets the necessary requirements for optimal performance, offering a promising solution for control system design