A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered.

Process Models of Development of a Triad of Subsystems of Medical and Preventive Institutions: Management, Resources, Potential. Control

Frontal analysis of the activities of medical and preventive institutions on the basis of the process model involves an objective assessment of the activities of all sections of preventive and therapeutic and diagnostic work, identification of shortcomings and reserves, foresight on this basis of the results of the work of medical and preventive institutions.

Enabling Production of Algal Biofuels by Techno-Economic Optimization of Co-Product Suites

Recent techno-economic analysis (TEA) has underscored that for algal biofuels to be cost competitive with petroleum fuels, co-products are necessary to offset the cost of fuel production. The co-product suite must scale with fuel production while also maximizing value from the non-fuel precursor components. The co-product suite also depends on algal biomass composition, which is highly dynamic and depends on environmental conditions during cultivation. Intentional shifts in composition during cultivation are often associated with reduced biomass productivity, which can increase feedstock production costs for the algae-based biorefinery. The optimal algae-based biorefinery configuration is thus a function of many factors. We have found that comprehensive TEA, which requires the construction of process models with detailed mass and energy balances, along with a complete accounting of capital and operating expenditures for a commercial-scale production facility, provides invaluable insight into the viability of a proposed biorefinery configuration. This insight is reflected in improved viability for one biorefining approach that we have developed over the last 10 years, namely, the Combined Algal Processing (CAP) approach. This approach fractionates algal biomass into carbohydrate-, lipid-, and protein-rich fractions, and tailors upgrading chemistry to the composition of each fraction. In particular, transitioning from valorization of only the lipids to a co-product suite from multiple components of high-carbohydrate algal biomass can reduce the minimum fuel selling price (MFSP) from more than $8/gallon of gasoline equivalent (GGE) to $2.50/GGE. This paper summarizes that progress and discusses several surprising implications in this optimization approach.

Seismic Source Processes of 25 Earthquakes (Mw>5) in the Gulf of California

ABSTRACT The Gulf of California (GoC) is a complex tectonic boundary that has been instrumented in the past several decades to record broadband seismograms. This volume of data has allowed us to study several source parameters systematically. Before, only a few source parameters of earthquakes greater than magnitude five had been studied in the GoC area. We re-examined the focal mechanisms of several earthquakes in the southern GoC that occurred over the last 20 yr using local–regional distance broadband seismograms. These focal mechanisms were then used as input data to retrieve the time–space history of the rupture for each earthquake. This work contributes to the study of 25 rupture-process models computed with the method proposed by Yagi et al. (1999). To investigate more about the nature of the seismicity in the GoC, we also calculated the non-double-couple component of moment tensors for 45 earthquakes. Previous studies (e.g., Ortega et al., 2013, 2016) have shown that non-double-couple components from moment tensors in this region are associated with complex faulting, suggesting that oblique faults or several parallel faults are interacting simultaneously. Our results show that, at least for moderate earthquakes (5 < M < 6), rupture processes in the GoC show a complex interaction between fault systems. It is revealed on the important contribution of non-double-couple component obtained in the full moment tensor analysis.

Automated Repair of Process Models with Non-local Constraints Using State-Based Region Theory

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.

An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students’ Learning Behavior and Improving Teaching Quality

This paper focuses on the study of automated process discovery using the Inductive visual Miner (IvM) and Directly Follows visual Miner (DFvM) algorithms to produce a valid process model for educational process mining in order to understand and predict the learning behavior of students. These models were evaluated on the publicly available xAPI (Experience API or Experience Application Programming Interface) dataset, which is an education dataset intended for tracking students’ classroom activities, participation in online communities, and performance. Experimental results with several performance measures show the effectiveness of the developed process models in helping experts to better understand students’ learning behavioral patterns.

Inferring Unobserved Events in Systems with Shared Resources and Queues

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to a variety of reasons, real-life systems often record only a subset of all events taking place. To understand and analyze the behavior and performance of processes with shared resources, we aim to reconstruct bounds for timestamps of events in a case that must have happened but were not recorded by inference over events in other cases in the system. We formulate and solve the problem by systematically introducing multi-entity concepts in event logs and process models. We introduce a partial-order based model of a multi-entity event log and a corresponding compositional model for multi-entity processes. We define PQR-systems as a special class of multi-entity processes with shared resources and queues. We then study the problem of inferring from an incomplete event log unobserved events and their timestamps that are globally consistent with a PQR-system. We solve the problem by reconstructing unobserved traces of resources and queues according to the PQR-model and derive bounds for their timestamps using a linear program. While the problem is illustrated for material handling systems like baggage handling systems in airports, the approach can be applied to other settings where recording is incomplete. The ideas have been implemented in ProM and were evaluated using both synthetic and real-life event logs.

Ultrasonic additive manufacturing of nanocrystalline laminated composites

Ultrasonic additive manufacturing has been used to fabricate laminated composites of commercially pure aluminum and a nanocrystalline nickel–cobalt (nc-NiCo) alloy. The nc-NiCo alloy would not weld to itself but readily welded to aluminum. Thus, by alternating between foils of nc-NiCo and Al, we achieved multi-material laminates with strong interlayer bonding. Electron microscopy showed that the nanoscale grain structure of the nc-NiCo was preserved during deposition and that the nc-NiCo/Al weld interface was decorated with comminuted surface oxides as well as Al–Ni–Co intermetallics. These findings are considered in light of process models of junction growth, interdiffusion, and grain growth, which together reveal how the different pressure- and temperature dependences of these phenomena give rise to a range of processing conditions that maximize bonding while minimizing coarsening and intermetallic formation. This analysis quantitatively demonstrates that using a soft, low melting point interlayer material decouples junction growth at the weld interface from grain growth in the nc-NiCo, expanding the range of optimal processing conditions. Graphical abstract