A hybrid moment equation approach to gas-grain chemical modeling

[Context] The stochasticity of grain chemistry requires special care in modeling. Previously methods based on the modified rate equation, the master equation, the moment equation, and Monte Carlo simulations have been used. [Aims] We attempt to develop a systematic and efficient way to model the gas-grain chemistry with a large reaction network as accurately as possible. [Methods] We present a hybrid moment equation approach which is a general and automatic method where the generating function is used to generate the moment equations. For large reaction networks, the moment equation is cut off at the second order, and a switch scheme is used when the average population of certain species reaches 1. For small networks, the third order moments can also be utilized to achieve a higher accuracy. [Results] For physical conditions in which the surface reactions are important, our method provides a major improvement over the rate equation approach, when benchmarked against the rigorous Monte Carlo results. For either very low or very high temperatures, or large grain radii, results from the rate equation are similar to those from our new approach. Our method is faster than the Monte Carlo approach, but slower than the rate equation approach. [Conclusions] The hybrid moment equation approach with a cutoff and switch scheme is applicable to large gas-grain networks, and is accurate enough to be used for astrochemistry studies. The layered structure of the grain mantle could also be incorporated into this approach, although a full implementation of the grain micro-physics appears to be difficult.Comment: 11 pages, 4 figures. Accepted for publication in Astronomy and Astrophysic

A framework to support a simulation-based understanding of digitalisation in remanufacturing operations

This is the author accepted manuscript.Modelling and simulations are important in predicting the response and behavior of manufacturing shop-floor operations such as predictive maintenance in relation to the real-life operations. Thus, remanufacturing operations, an end-of-life operation focused on returning a “disassemble-able” product to a condition which is at least as new as the original specification, can be influenced by modelling and simulation. While simulations have a limitation in their ability to enable real-time business decisions in environments of complexity due to costs and time required to build these models, remanufacturing operations in particular will benefit from the application of simulations. As remanufacturing is characterized by an uncertain nature of product returns, simulation modelling can be used to support the understanding of different methods from a real-time scenario context. With manufacturing digitalization, complexity in remanufacturing is further increased with more data produced as sensor-enabled products enter the remanufacturing shop-floor. This paper investigates how modelling and simulation could be used to provide clarity to the digitalization of remanufacturing operations and proposes a framework to support simulation modelling for remanufacturing sensor-enabled products. Findings from the synthesis of a systematic literature review and five remanufacturing case studies reveal that system dynamics modelling has greater application to remanufacturing over other modelling techniques. Additionally, the importance of digitalisation across the six stages ofremanufacturing is expected to be similar and, as such, reduces medium term cost implications for remanufacturers looking to digitalise

A vision of re-distributed manufacturing for the UK’s consumer goods industry

The linear production of consumer goods is characterised by mass manufacture, multinational enterprises and globally dispersed supply chains. Redistributed manufacture (RDM) is an emerging topic, which seeks to enable a transition of the current linear model of production and consumption, by taking advantage of new technologies. This paper aims to explore the challenges, opportunities and further research questions to set a vision of Redistributed manufacturing for the UK’s consumer goods industry. To set this vision, a literature survey was conducted followed by a qualitative enquiry where PESTLE1 aspects of RDM were analysed. This analysis was interpreted through a roadmap. As a result of this roadmap, four RDM characteristics (i.e. customisation, use of digital technologies, local production and the development of new business models) were identified. These characteristics helped to set the future vision of RDM in the UK’s consumer goods sector

Towards a simulation-based understanding of smart remanufacturing operations : a comparative analysis

While the majority of literature on remanufacturing operations examines an end-of-life (EOL) strategy which is both manual and mechanised, authors generally agree that digitalisation of remanufacturing is expected to increase in the next decade. Subsequently, a new research area described as digitally-enabled remanufacturing, remanufacturing 4.0 or smart remanufacturing is emerging. This is an automated, data-driven system of remanufacturing by means of Industry 4.0 (I4.0) paradigms. Insights into smart remanufacturing can be provided through simulation modelling of the remanufacturing process. While the use of simulation modelling in order to predict responses and behaviour is prevalent in remanufacturing, the use of these tools in smart remanufacturing is still limited in literature. The goal of this research is to present, as a first of its kind, a comparative understanding of simulation modelling in remanufacturing in order to suggest the ideal modelling tool for smart remanufacturing. The proposed comparison includes system dynamics, discrete event simulation and agent based modelling techniques. We apply these modelling techniques on a smart remanufacturing space of a sensor-enabled product and use assumptions derived from industry experts. We then proceed to model the remanufacturing operation from sorting and inspection of cores to final inspection of the remanufactured product. Through our analysis of the assumptions utilised and simulation modelling results we conclude that, while individual modelling techniques present important strategic and operational insights, their individual use may not be sufficient to offer comprehensive knowledge to remanufacturers due to the challenge of data complexity that smart remanufacturing offers

Digitisation and the Circular Economy: A Review of Current Research and Future Trends

This is the final verson. Available on open access from MDPI via the DOI in this recordSince it first appeared in literature in the early nineties, the Circular Economy (CE) has grown in significance amongst academic, policymaking, and industry groups. The latest developments in the CE field have included the interrogation of CE as a paradigm, and its relationship with sustainability and other concepts, including iterative definitions. Research has also identified a significant opportunity to apply circular approaches to our rapidly changing industrial system, including manufacturing processes and Industry 4.0 (I4.0) which, with data, is enabling the latest advances in digital technologies (DT). Research which fuses these two areas has not been extensively explored. This is the first paper to provide a synergistic and integrative CE-DT framework which offers directions for policymakers and guidance for future research through a review of the integrated fields of CE and I4.0. To achieve this, a Systematic Literature Review (SLR; n = 174) of the empirical literature related to digital technologies, I4.0, and circular approaches is conducted. The SLR is based on peer-reviewed articles published between 2000 and early 2018. This paper also summarizes the current trends in CE research related to manufacturing. The findings confirm that while CE research has been on the increase, research on digital technologies to enable a CE is still relatively untouched. While the “interdisciplinarity” of CE research is well-known, the findings reveal that a substantial percentage is engineering-focused. The paper concludes by proposing a synergistic and integrative CE-DT framework for future research developed from the gaps in the current research landscapeEngineering and Physical Sciences Research Council (EPSRC

Simulation to enable a data-driven circular economy

This is the final version. Available on open access from MDPI via the DOI in this record.The underlying data can be accessed at 10.15131/shef.data.8246912This paper presents an investigation on how simulation informed by the latest advances in digital technologies such as the 4th Industrial Revolution (I4.0) and the Internet of Things (IoT) can provide digital intelligence to accelerate the implementation of more circular approaches in UK manufacturing. Through this research, a remanufacturing process was mapped and simulated using discrete event simulation (DES) to depict the decision-making process at the shop-floor level of a remanufacturing facility. To understand the challenge of using data in remanufacturing, a series of interviews were conducted finding that there was a significant variability in the condition of the returned product. To address this gap, the concept of certainty of product quality (CPQ) was developed and tested through a system dynamics (SD) and DES model to better understand the effects of CPQ on products awaiting remanufacture, including inspection, cleaning and disassembly times. The wider application of CPQ could be used to forecast remanufacturing and production processes, resulting in reduced costs by using an automatised process for inspection, thus allowing more detailed distinction between “go” or “no go” for remanufacture. Within the context of a circular economy, CPQ could be replicated to assess interventions in the product lifecycle, and therefore the identification of the optimal CE strategy and the time of intervention for the current life of a product—that is, when to upgrade, refurbish, remanufacture or recycle. The novelty of this research lies in investigating the application of simulation through the lens of a restorative circular economic model focusing on product life extension and its suitability at a particular point in a product’s life cycle.Engineering and Physical Sciences Research Council (EPSRC)Royal Academy of Engineering (RAEng)Airbu

Incorporation of stochastic chemistry on dust grains in the PDR code using moment equations

Unlike gas-phase reactions, chemical reactions taking place on interstellar dust grain surfaces cannot always be modeled by rate equations. Due to the small grain sizes and low flux,these reactions may exhibit large fluctuations and thus require stochastic methods such as the moment equations. We evaluate the formation rates of H2, HD and D2 molecules on dust grain surfaces and their abundances in the gas phase under interstellar conditions. We incorporate the moment equations into the Meudon PDR code and compare the results with those obtained from the rate equations. We find that within the experimental constraints on the energy barriers for diffusion and desorption and for the density of adsorption sites on the grain surface, H2, HD and D2 molecules can be formed efficiently on dust grains. Under a broad range of conditions, the moment equation results coincide with those obtained from the rate equations. However, in a range of relatively high grain temperatures, there are significant deviations. In this range, the rate equations fail while the moment equations provide accurate results. The incorporation of the moment equations into the PDR code can be extended to other reactions taking place on grain surfaces

Organic Molecules in Low-Mass Protostellar Hot Cores: Submillimeter Imaging of IRAS 16293-2422

Arcsecond-resolution spectral observations toward the protobinary system IRAS 16293-2422 at 344 and 354 GHz were conducted using the Submillimeter Array. Complex organic molecules such as CH3OH and HCOOCH3 were detected. Together with the rich organic inventory revealed, it clearly indicates the existence of two, rather than one, compact hot molecular cores (smaller than or equal to 400 AU in radius) associated with each of the protobinary components identified by their dust continuum emission in the inner star-forming core.Comment: 11 pages, 3 figures, to be published in ApJ

Physical-chemical modeling of the low-mass protostar IRAS 16293-2422

We present detailed gas-phase chemical models for the envelope of the low-mass star-forming region IRAS 16293-2422. By considering both time- and space-dependent chemistry, these models are used to study both the physical structure proposed by Schoier et al. (2002), as well as the chemical evolution of this region. A new feature of our study is the use of a detailed, self-consistent radiative transfer model to translate the model abundances into line strengths and compare them directly with observations of a total of 76 transitions for 18 chemical species, and their isotopes. The model can reproduce many of the line strengths observed within 50%. The best fit is for times in the range of 3e3 - 3e4 yrs, and requires only minor modifications to our model for the high-mass star-forming region AFGL 2591. The ionization rate for the source may be higher than previously expected -- either due to an enhanced cosmic-ray ionization rate, or, more probably, to the presence of X-ray induced ionization from the center. A significant fraction of the CO is found to desorb in the temperature range of 15-40 K; below this temperature, \~90% or more of the CO is frozen out. The inability of the model to explain the HCS+, C2H, and OCS abundances suggests the importance of further laboratory studies of basic reaction rates. Finally, predictions of the abundances and spatial distributions of other species which could be observed by future facilities (e.g., Herschel-HIFI, SOFIA, millimeter arrays) are provided.Comment: 15 pages, 11 Figures, accepted for publication by A&

Chemical sensitivity to the ratio of the cosmic-ray ionization rates of He and H2 in dense clouds

Aim: To determine whether or not gas-phase chemical models with homogeneous and time-independent physical conditions explain the many observed molecular abundances in astrophysical sources, it is crucial to estimate the uncertainties in the calculated abundances and compare them with the observed abundances and their uncertainties. Non linear amplification of the error and bifurcation may limit the applicability of chemical models. Here we study such effects on dense cloud chemistry. Method: Using a previously studied approach to uncertainties based on the representation of rate coefficient errors as log normal distributions, we attempted to apply our approach using as input a variety of different elemental abundances from those studied previously. In this approach, all rate coefficients are varied randomly within their log normal (Gaussian) distribution, and the time-dependent chemistry calculated anew many times so as to obtain good statistics for the uncertainties in the calculated abundances. Results: Starting with so-called ``high-metal'' elemental abundances, we found bimodal rather than Gaussian like distributions for the abundances of many species and traced these strange distributions to an extreme sensitivity of the system to changes in the ratio of the cosmic ray ionization rate zeta\_He for He and that for molecular hydrogen zeta\_H2. The sensitivity can be so extreme as to cause a region of bistability, which was subsequently found to be more extensive for another choice of elemental abundances. To the best of our knowledge, the bistable solutions found in this way are the same as found previously by other authors, but it is best to think of the ratio zeta\_He/zeta\_H2 as a control parameter perpendicular to the ''standard'' control parameter zeta/n\_H.Comment: Accepted for publicatio