Готові списки джерел за темами / Network co-production model

Добірка наукової літератури з теми "Network co-production model"

Автор: Grafiati

Опубліковано: 19 червня 2021

Оновлено: 4 лютого 2022

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Статті в журналах з теми "Network co-production model":

Busacca, Maurizio, and Flaviano Zandonai. "Platform social enterprises as a new model for co-production." STUDI ORGANIZZATIVI, no. 2 (December 2020): 61–86. http://dx.doi.org/10.3280/so2020-002003.

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The paper analyses the mechanisms of organisational integration in two social enterprises characterised by a strong orientation towards the creation and management of networks with the aim of gaining useful learning in the framework of the emerging debate on production and governance models that characterise platform organisations, in particular in the digital context. In order to do this, it deepens the dimensions of leadership, organisational knowledge and production in two Italian cases: Le Case del Quartiere, a network of social infrastructures in Turin, and TreCuori, a company and territorial welfare agency in Veneto. The two cases are analysed according to the literature of organisational studies which, since the 1980s, has deepened the issue of the progressive narrowing of organisational boundaries and the increase in interdependence between units and organisations variously located with respect to those boundaries. The main evidence that emerged from the case studies are two. The first, in contrast with the rhetoric of disintermediation associated with the sharing economy, identifies the key role of intermediaries who position themselves as third parties with respect to the demand and supply of goods and services in order to facilitate their meeting and, at the same time, foster the mechanisms of entrepreneurial use of knowledge and relationships. The second, with significant theoretical implications, identifies the "platform social enterprise" as an organisational model that introduces strong traits of cooperation in the relational systems that characterise mainstream platform-enterprises. The findings of the investigation offer an original contribution to the convergence between social innovation, collaborative economics and new governance models studies, with a shift from "platform capitalism" to "platform cooperativism" by organizations that use place-based social innovation models and give importance not only to relational and political-cultural factors, but also to co-production, co-working, collaboration and networking. Observed from this perspective, the platform social enterprise becomes a model to be more considered in order to propose a more cooperative, sustainable and democratic development trajectory of platforms.

Mezgár, István, and George L. Kovács. "PLENT: A European Project on SME co-operation." Human Systems Management 18, no. 3-4 (December 29, 1999): 193–201. http://dx.doi.org/10.3233/hsm-1999-183-404.

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Today enterprises expand their activities and co-operate with each other in order to adapt their production to the fast changing market environment. The different forms of co-operation need new management approaches from the individual enterprises, to co-ordinate the work of the different manufacturing organisations. A new, co-operative manufacturing network model is proposed for co-ordinating the production of SMEs, based on the holonic paradigm. The model has been developed in the frame of an ESPRIT Project by four countries. The paper shortly describes the main modules of the model and introduces an application under realisation in Hungary, in an agricultural SME network.

Zeng, Guang Sheng, Hui Zhang, and Hua Chen. "The Forecast of Co-Rotating Twin-Screw Extruder's Screw Based on BP Neural Network Model." Applied Mechanics and Materials 711 (December 2014): 257–60. http://dx.doi.org/10.4028/www.scientific.net/amm.711.257.

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The established BP neural network model can forecast one of the wood-plastic composite molding equipment the screw of extruder with co-rotating twin-screw has been presented. The content of fiber and lubricant, the appropriate temperature of the extruder and the target yield are determined as the variable inputs, while the outputs are the diameters of the screw stem. This intelligent BP network model can forecast the condition of actual production precisely by setting the input properties of the material and the integration of products in order to fit the requirements of the wood-plastic composited production more successfully. Through the analysis of BP neural network model, the former simple method of choosing the screw stem’ diameters only depend on yield can be modified more efficiently.

Zeng, G., J. E. Williams, J. A. Fisher, L. K. Emmons, N. B. Jones, O. Morgenstern, J. Robinson, D. Smale, C. Paton-Walsh, and D. W. T. Griffith. "Multi-model simulation of CO and HCHO in the Southern Hemisphere: biogenic emissions and model uncertainties." Atmospheric Chemistry and Physics Discussions 15, no. 2 (January 27, 2015): 2615–78. http://dx.doi.org/10.5194/acpd-15-2615-2015.

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Abstract. We investigate the impact of biogenic emissions on carbon monoxide (CO) and formaldehyde (HCHO) in the Southern Hemisphere (SH), with simulations using two different biogenic emission inventories for isoprene and monoterpenes. Results from four atmospheric chemistry models are compared to continous long-term ground-based CO and HCHO column measurements at SH NDACC sites, and to in situ surface CO measurements from across the SH, representing a subset of the NOAA GMD network. Simulated mean model CO using the CLM-MEGANv2.1 inventory is in good agreement with both column and surface observations, whereas simulations adopting LPJ-GUESS emissions markedly underestimate measured column and surface CO at most sites. Differences in biogenic emissions cause large differences in CO in the source regions which propagate to the remote SH. Significant inter-model differences exist in modelled column and surface CO, due mainly to differences in the models' oxidation schemes for volatile organic compounds; secondary production of CO dominates these inter-model differences. While biogenic emissions are a significant factor in modelling SH CO, inter-model differences pose an additional challenge to constrain these emissions. Corresponding comparisons of HCHO columns at two SH mid-latitude sites reveal that all models significantly underestimate the observed values by approximately a factor of 2. There is a much smaller impact on HCHO of the significantly different biogenic emissions in remote regions, compared to the source regions. Decreased biogenic emissions cause decreased CO export to remote regions, which leads to increased OH; this in turn results in increased HCHO production through methane oxidation. In agreement with earlier studies, we corroborate that significant HCHO sources are likely missing in the models in the remote SH.

Ghatak, Manjula Das, and Amitava Ghatak. "Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates." Fuel 232 (November 2018): 178–89. http://dx.doi.org/10.1016/j.fuel.2018.05.051.

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Hewitt, Gillian, Joan S. Roberts, Adam Fletcher, Graham Moore, and Simon Murphy. "Improving young people's health and well-being through a school health research network: Reflections on school–researcher engagement at the national level." Research for All 2, no. 1 (January 25, 2018): 16–33. http://dx.doi.org/10.18546/rfa.02.1.03.

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The School Health Research Network is a policy–practice–research partnership established in Wales in 2013. The network aims to: provide health and well-being data for national, regional and local stakeholders, including schools; co-produce school-based health improvement research for Wales; and build capacity for evidence-informed practice in the school health community. School-focused engagement activities include providing member schools with bespoke Student Health and Well-being Reports, hosting school health webinars, producing schoolfriendly research briefings and holding annual events for schools. The network's model for co-producing research with schools is described and its impacts on schools is explored. These include more efficient recruitment of schools to research projects, school involvement in intervention development, schools beginning to embed evidence-informed practice by using their Health and Well-being Reports and other network resources, and securing funding to evaluate innovative health and well-being practices identified by schools. Drawing on the transdisciplinary action research (TDAR) literature, the article reflects on how TDAR principles have underpinned the progress of the network. The concept of reciprocity in the co-production literature, and its relevance to engagement with schools, is also explored, along with the network's contribution to our understanding of how we can build sustainable co-production at large scale in order to generate nationallevel action and benefit.

Zhong, Zhi, Alexander Y. Sun, Bo Ren, and Yanyong Wang. "A Deep-Learning-Based Approach for Reservoir Production Forecast under Uncertainty." SPE Journal 26, no. 03 (January 18, 2021): 1314–40. http://dx.doi.org/10.2118/205000-pa.

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Summary This paper presents a deep-learning-based proxy modeling approach to efficiently forecast reservoir pressure and fluid saturation in heterogeneous reservoirs during waterflooding. The proxy model is built on a recently developed deep-learning framework, the coupled generative adversarial network (Co-GAN), to learn the joint distribution of multidomain high-dimensional image data. In our formulation, the inputs include reservoir static properties (permeability), injection rates, and forecast time, while the outputs include the reservoir dynamic states (i.e., reservoir pressure and fluid saturation) corresponding to the forecast time. Training data obtained from full-scale numerical reservoir simulations were used to train the Co-GAN proxy model, and then testing data were used to evaluate the accuracy and generalization ability of the trained model. Results indicate that the Co-GAN proxy model can predict the reservoir pressure and fluid saturation with high accuracy, which in turn, enable accurate predictions of well production rates. Moreover, the Co-GAN proxy model also is robust in extrapolating dynamic reservoir states. The deep-learning proxy models developed in this work provide a new and fast alternative to estimating reservoir production in real time.

Zeng, G., J. E. Williams, J. A. Fisher, L. K. Emmons, N. B. Jones, O. Morgenstern, J. Robinson, D. Smale, C. Paton-Walsh, and D. W. T. Griffith. "Multi-model simulation of CO and HCHO in the Southern Hemisphere: comparison with observations and impact of biogenic emissions." Atmospheric Chemistry and Physics 15, no. 13 (July 2, 2015): 7217–45. http://dx.doi.org/10.5194/acp-15-7217-2015.

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Abstract. We investigate the impact of biogenic emissions on carbon monoxide (CO) and formaldehyde (HCHO) in the Southern Hemisphere (SH), with simulations using two different biogenic emission inventories for isoprene and monoterpenes. Results from four atmospheric chemistry models are compared to continuous long-term ground-based CO and HCHO column measurements at the SH Network for the Detection of Atmospheric Composition Change (NDACC) sites, the satellite measurement of tropospheric CO columns from the Measurement of Pollution in the Troposphere (MOPITT), and in situ surface CO measurements from across the SH, representing a subset of the National Oceanic and Atmospheric Administration's Global Monitoring Division (NOAA GMD) network. Simulated mean model CO using the Model of Emissions of Gases and Aerosols from Nature (v2.1) computed in the frame work of the Land Community Model (CLM-MEGANv2.1) inventory is in better agreement with both column and surface observations than simulations adopting the emission inventory generated from the LPJ-GUESS dynamical vegetation model framework, which markedly underestimate measured column and surface CO at most sites. Differences in biogenic emissions cause large differences in CO in the source regions which propagate to the remote SH. Significant inter-model differences exist in modelled column and surface CO, and secondary production of CO dominates these inter-model differences, due mainly to differences in the models' oxidation schemes for volatile organic compounds, predominantly isoprene oxidation. While biogenic emissions are a significant factor in modelling SH CO, inter-model differences pose an additional challenge to constrain these emissions. Corresponding comparisons of HCHO columns at two SH mid-latitude sites reveal that all models significantly underestimate the observed values by approximately a factor of 2. There is a much smaller impact on HCHO of the significantly different biogenic emissions in remote regions, compared to the source regions. Decreased biogenic emissions cause decreased CO export to remote regions, which leads to increased OH; this in turn results in increased HCHO production through methane oxidation. In agreement with earlier studies, we corroborate that significant HCHO sources are likely missing in the models in the remote SH.

Moreno Cárdenas, Edilson León, Arley David Zapata-Zapata, and Daehwan Kim. "Modeling Dark Fermentation of Coffee Mucilage Wastes for Hydrogen Production: Artificial Neural Network Model vs. Fuzzy Logic Model." Energies 13, no. 7 (April 2, 2020): 1663. http://dx.doi.org/10.3390/en13071663.

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This study presents the analysis and estimation of the hydrogen production from coffee mucilage mixed with organic wastes by dark anaerobic fermentation in a co-digestion system using an artificial neural network and fuzzy logic model. Different ratios of organic wastes (vegetal and fruit garbage) were added and combined with coffee mucilage, which led to an increase of the total hydrogen yield by providing proper sources of carbon, nitrogen, mineral, and other nutrients. A two-level factorial experiment was designed and conducted with independent variables of mucilage/organic wastes ratio, chemical oxygen demand (COD), acidification time, pH, and temperature in a 20-L bioreactor in order to demonstrate the predictive capability of two analytical modeling approaches. An artificial neural network configuration of three layers with 5-10-1 neurons was developed. The trapezoidal fuzzy functions and an inference system in the IF-THEN format were applied for the fuzzy logic model. The quality fit between experimental hydrogen productions and analytical predictions exhibited a predictive performance on the accumulative hydrogen yield with the correlation coefficient (R2) for the artificial neural network (> 0.7866) and fuzzy logic model (> 0.8485), respectively. Further tests of anaerobic dark fermentation with predefined factors at given experimental conditions showed that fuzzy logic model predictions had a higher quality of fit (R2 > 0.9508) than those from the artificial neural network model (R2 > 0.8369). The findings of this study confirm that coffee mucilage is a potential resource as the renewable energy carrier, and the fuzzy-logic-based model is able to predict hydrogen production with a satisfactory correlation coefficient, which is more sensitive than the predictive capacity of the artificial neural network model.

Zhang, Lei, Hai Gang Xu, Chao Zhang, and Chao Wei Duan. "Research for Coiling Temperature Control System and Mathematical Model on Hot Strip Mill." Advanced Materials Research 411 (November 2011): 274–78. http://dx.doi.org/10.4028/www.scientific.net/amr.411.274.

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Laminar Cooling is an indispensable part of hot strip steel mill. Based on the devices, instruments and control requirements of 1150mm hot strip steel mill in a certain actual Iron & Steel Co., the laminar cooling control system is designed, including hardware figuration, network framework, software functions, mathematical model, etc. The engineering practice proves that the control system is steady and reliable, and it has the value being popularized in the other similar production line.

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Дисертації з теми "Network co-production model":

Tontodonato, Richard Edward. "Co-production of Science and Regulation: Radiation Health and the Linear No-Threshold Model." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103886.

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The model used as the basis for regulation of human radiation exposures in the United States has been a source of controversy for decades because human health consequences have not been determined with statistically meaningful certainty for the dose levels allowed for radiation workers and the general public. This dissertation evaluates the evolution of the science and regulation of radiation health effects in the United States since the early 1900s using actor-network theory and the concept of co-production of science and social order. This approach elucidated the ordering instruments that operated at the nexus of the social and the natural in making institutions, identities, discourses, and representations, and the sociotechnical imaginaries animating the use of those instruments, that culminated in a regulatory system centered on the linear no-threshold dose-response model and the As Low As Reasonably Achievable philosophy. The science of radiation health effects evolved in parallel with the development of radiation-related technologies and the associated regulatory system. History shows the principle of using the least amount of radiation exposure needed to achieve the desired effect became established as a social convention to help avoid inadvertent harm long before there was a linear no-threshold dose-response model. Because of the practical need to accept some level of occupational radiation exposure, exposures from medical applications of radiation, and some de minimis exposure to the general public, the ALARA principle emerged as an important ordering instrument even before the linear no-threshold model had gained wide support. Even before ALARA became the law, it had taken hold in a manner that allowed the nuclear industry to rationalize its operations as representing acceptable levels of risk, even though it could not be proven that the established exposure limits truly precluded harm to the exposed individuals. Laboratory experiments and epidemiology indicated that a linear dose-response model appeared suitable as a "cautious assumption" by the 1950s. The linear no-threshold model proved useful to both the nuclear establishment and its detractors. In the hands of proponents of nuclear technologies, the model predicted that occupational exposures and exposures to the public represented small risks compared to naturally occurring levels of radiation and other risks that society deemed acceptable. Conversely, opponents of nuclear technologies used the model to advance their causes by predicting health impacts for undesirable numbers of people if large populations received small radiation exposures from sources such as fallout from nuclear weapon testing or effluents from nuclear reactor operations. In terms of sociotechnical imaginaries, the linear no-threshold model was compatible with both of the dominant imaginaries involved in the actor-network. In the technocratic imaginary of institutions such as the Atomic Energy Commission, the model served as a tool for qualified experts to make risk-informed decisions about applications of nuclear technologies. In the socially progressive imaginary of the citizen activist groups, the model empowered citizens to formulate arguments informed by science and rooted in the precautionary principle to challenge decisions and actions by the technocratic institutions. This enduring dynamic tension has led to the model retaining the status of "unproven but useful" even as the underlying science has remained contested.
Doctor of Philosophy
This dissertation provides a social science perspective on an enduring paradox of the nuclear industry: why is regulation of radiation exposure based on a model that everyone involved agrees is wrong? To answer that question, it was necessary to delve into the history of radiation science to establish how safety regulation began and evolved along with the understanding of radiation's health effects. History shows the philosophy of keeping radiation exposures as small as possible for any given application developed long ago when the health effects of radiation were very uncertain. This practice turned out to be essential as science started to indicate that there may not be a safe threshold dose below which radiation exposure had no potential for health consequences. By the 1950s, a combination of theory, experiments, health studies of the survivors of the World War II atomic bombings, and other evidence suggested that the risk of cancer was proportional to the amount of radiation a person received (i.e., linear). Although this "linear no-threshold" model was far from proven, both sides used it in debates over nuclear weapon testing and safety standards for nuclear reactors in the 1950s through the early 1970s. Since the model predicted small health risks for the levels of radiation experienced by radiation workers and the public, nuclear advocates used it to argue that the risks were smaller than many other risks that people accept every day. At the same time, opposing activists used the model to argue that small cancer likelihoods added up to a lot of cancers when large populations were exposed. This decades-long discourse effectively institutionalized the model. The model's "unproven but useful" status was strengthened in the early 1970s when the Atomic Energy Commission supplemented its numeric exposure limits by turning the longtime practice of dose minimization into a requirement. This "As Low As Reasonably Achievable" requirement plays a vital role in rationalizing why a non-zero exposure limit is safe enough despite the fact that the linear no-threshold model treats any amount of radiation as harmful.

Näsberg, Victoria. "Internationalization and its role in the utilization of Influencer Marketing within Business." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85284.

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The growth within internationalization has changed the world at a rapid pace, with businesses expanding into foreign markets on a daily basis. Similarly, to the growth within internationalization, social media has changed how marketing activities are conducted throughout the world. Influencer marketing has been one of those drivers of growth, changing the way that brands market themselves on a global basis. Therefore, there is a need to understand the relationship between internationalization and influencer marketing but determining the role that internationalization plays in the way that influencer marketing is used within businesses that plan on expanding to new markets. In relation to how traditional marketing tools are adapted through the use of influencer marketing during the internationalization process. For the data collection two interviews were conducted along with one questionnaire with 109 respondents. The study has followed the same structure as can be seen in the frame of reference, according to two main subjects: Internationalization and E-marketing. The findings gathered through the questionnaires showed that credibility was highly valued in relation to that the use of local influencers was preferred to increase trust and credibility between the brand, influencer and their target audience. Along with credibility being more valued by the respondents than an influencer's network size. The findings from the interviews show that the use of influencer marketing is greatly dependent on the needs and preference of the individual market. While one brand preferred a more united front, yet still adapting to the Chinese market in terms of using local influencers and social media platforms. The other brand favored using local influencers in their different markets in order to best reach their consumers. Influencer marketing was used by the two brands to build brand awareness in new markets, through different influencer networks, yet the size of the network was not important for one of the brands, instead it was the connections within the network that were important.

Hsu, Wen Chuan, and 許文娟. "The Study of Value Co-creation of The Production Committee Model in Japan by Actor-Network Theory: Implications for Taiwan's Original Comics in Digital Content and Co-creation Community Development." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/48s3c7.

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碩士
國立清華大學
高階經營管理碩士在職專班
103
Impacted by the evolution of information technology, digital content has been the major form of modern cultural development. The path of the software and hardware development for digital content industry in Taiwan is comparatively fast. However, the industry still struggles for the creation of original content as well as the right business models for the ever changing markets. The former is resulted from the education and the cultural attainment of profession, and the latter is the key successful factor for improving the total value of the industry. It has been a normal practice the people directly use computer tools to create digital content and then publish their works through the digital platforms on the internet. Content producers become the integrators to orchestrate the different teams of storytelling, visual effect, musical composition, and user interface design. All these professional services as mentioned above accomplish the core of the original content. The profit sharing mechanism for all the actors in the network is achieved by interconnecting the professional services in the value chain of the industry. In the domain of cultural creation and digital content, comic is a fundamental material. This research discusses the possible alternative ways to reform the value chain of the digital content industry in Taiwan by investigating the Production Committee Model in Japan and applying the Actor Network Theory. The goal of this research is to find out the methods, workflows and driving factors to make an innovated service system for comic industry in Taiwan. This research shows the insight of providing a feasible way to form a new industry value chain for the digital content industry in Taiwan.

Частини книг з теми "Network co-production model":

Demiray, Melek, Sebnem Burnaz, and Yonca Aslanbay. "The Crowdfunding Market, Models, Platforms, and Projects." In Crowdsourcing, 115–51. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8362-2.ch007.

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The changing structure and intensity of competition on one hand, the ongoing pressure on creating differentiated offerings lead to seek out new ways to improve technology as well as new ways to perform business. Technological developments enable market participants or other interest parties to engage in the process of both innovation and production, resulting with an online social network market model of co-creation. This chapter focuses on describing different aspects of recently developing crowdfunding market models where new projects are offered and funded by large number of individuals from general public. The current extent and dynamics of the growing crowdfunding market, various models of crowdfunding, crowdfunding platforms and the projects that are crowdfunded are analyzed through the chapter by giving examples and comparative comments. The analysis of the market is concluded by the foreseen future trends.

Demiray, Melek, Sebnem Burnaz, and Yonca Aslanbay. "The Crowdfunding Market, Models, Platforms, and Projects." In Crowdfunding for Sustainable Entrepreneurship and Innovation, 90–126. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0568-6.ch006.

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Carroll, Noel. "Open Innovation." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–22. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6485-2.ch001.

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Software engineering is largely concerned with the methodical, systematic production of quality software. Despite significant advances in technology over the last decade, software engineering still heavily relies on human efforts and human interaction to generate economic contributions. In more recent years, the question of software service complexity has become central to Global Software Development (GSD). However, few efforts have surfaced to challenge the complexity of the relational infrastructure of software teams that support the development of software architecture. This is important in order to sustain and support lean software development organisational structures particularly in an open service innovation environment. However, from a socio-economic perspective, there are few theoretical efforts that attempt to introduce new insights on how the human factors contribute towards a GSD value co-creation. The objective of this chapter is to examine the application of Social Network Analysis (SNA) and i* (i star) modelling techniques to examine how we could model the economic impact of software relational structures. The chapter explores how i* models leverage SNA concepts to model GSD. This chapter also offers a discussion on the theoretical development of the socio-economics of GSD in an “open innovation” context.

Carroll, Noel. "Open Innovation." In Intelligent Systems, 31–53. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5643-5.ch002.

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Software engineering is largely concerned with the methodical, systematic production of quality software to support organizational goals. Despite significant advances in technology over the last decade, software engineering still heavily relies on human efforts and human interaction to generate economic contributions for an organization. In more recent years, the question of software service complexity has become central to Global Software Development (GSD) with a view of developing software across a global landscape. However, few efforts have surfaced to challenge the complexity of the relational infrastructure of software teams that support the development of software architecture. This is important in order to sustain and support lean software development and organisational structures particularly in an open service innovation environment. However, from a socio-economic perspective, there are few theoretical efforts that attempt to introduce new insights on how the human factors contribute towards a GSD value co-creation. The objective of this chapter is to examine the application of Social Network Analysis (SNA) and i* (i star) modelling techniques to examine how we could model the economic impact of software relational structures. The chapter explores how i* models leverage SNA concepts to model GSD development. This chapter also offers a discussion on the theoretical development of the socio-economics of GSD in an “open innovation” context.

Harwood, Tracy. "Convergence of Online Gaming and E-Commerce." In Advances in Electronic Commerce, 61–89. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-808-7.ch004.

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This chapter firstly explores the characteristics and features of online games and their potential for e-commercial exploitation via the communities of interest that have evolved within and around the gaming environments. The chapter then considers the most common models of e-commercialization applied in online games, virtual and mirror worlds by developers, outlining the types of relationships that exist between organizations and consumers, and the issues of co-creation and co-production that exist within each of the relational contexts identified. Finally, the chapter will review the emergence and convergence of online gaming economies and the issues presented at the boundary between real and fictional worlds, such as managing currencies, social identities and relational networks.

García, Juan de Dios. "Knowledge and Technologies for Learning and Action Network." In ICT Management in Non-Profit Organizations, 1–16. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5974-2.ch001.

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The experience gained over the last 20 years of working with the Third Sector (in different organizational, sectoral, and territorial areas), as well as the study of literature, invites us to reflect and analyze, in a world which is complex, uncertain, and with plenty of information and knowledge, on the need to produce a new innovation and transformation strategy. This “Great Transformation” will allow NGOs to have a greater global impact thanks to the development of new approaches, new relational models and the creation of value with their environment and stakeholders. To this end, this chapter analyzes and makes a concrete proposal about the role that technology (especially the Internet) and social networks can play in the co-production and distribution of knowledge and the role of participation in communities to learn and innovate, but in order to move forward in this challenge, requires addressing the difficulties and reluctance in the NGOs, which are not only technological, but conceptual, structural, relational, and cultural. As stated in the conclusion of the chapter, the key to success lies in the purpose and vision of what is to be achieved rather than on the technology used.

Browne, Pierson, and Brian R. Schram. "Intermediating the Everyday : Indie Game Development and the Labour of Co-Working Spaces." In Game Production Studies. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789463725439_ch04.

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Emblematic of major cultural and economic shifts towards ‘new work,’ indie game development has positioned itself at the forefront of market innovation by subverting traditional, hierarchical models of workplace organization. At the centre of these major shifts is the figure of the ‘cultural intermediary’ – a nebulous, ill-defined role which we, nonetheless, contend is integral to understanding cultural industries. By focusing on the mercurial forms of labour performed by founders and directors of indie co-working spaces, this chapter aims to give shape and dimension to the role of cultural intermediaries, arguing that their networked mobility and delamination from traditional ‘sites’ of work necessitates a rethinking of studio-based study as the standard for examining indie cultural production.

Leisink, Peter, Lotte B. Andersen, Christian B. Jacobsen, Eva Knies, Gene A. Brewer, and Wouter Vandenabeele. "Conclusion." In Managing for Public Service Performance, 297–316. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192893420.003.0016.

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The concluding chapter synthesizes the insights and gives a comprehensive answer to the volume’s overall question. It sets directions for future research and discusses implications for public organizations’ practice. There is ample evidence that management contributes to performance, both directly and indirectly, through influencing employees’ (public service) motivation, organizational commitment, and job performance. There is also evidence that management contributes to employee outcomes, both positive, such as their job satisfaction and employability, and negative, such as stress and burnout. The chapter reflects critically on the state of public management research and outlines four key issues for future research: (1) work toward an integrated theoretical framework; (2) develop more comprehensive theoretical models; (3) pay attention to the public sector context; and (4) increase methodological rigor. The chapter contends that public management–performance research remains relevant in the era of inter-organizational networks and co-production, if and when studies pay explicit attention to the public sector context and to the frontline employees involved in service production. The chapter advises public organizations to invest in service provision policies that fit the organizational mission and create the conditions for their implementation by frontline managers who can help public employees create public value.

"large audience” (Goldstein 1983: 26); and “Here was an Australian with a wry sense of humor and gruff charm [this was post-Crocodile Dundee], equally alluring to men and women” (Brown 1987: 33). In other words, Robert Scorpio is conveniently – if not tokenistically – played by an Australian. The limits of tolerance of the non-American for the world of network soap are instanced in General Hospital’s casting criteria for an (American) actor to play Robert Scorpio’s long-lost brother, Malcolm. The actor, John J. York, is quoted in the ABC house journal, Episodes, saying: “They didn’t want a strong dialect [sic] . . . . They didn’t want a Paul Hogan type, because that accent is too strong. They were saying ‘just a hint’” (Kump 1991: 29). The Australian is more “exotic” than Peter Pinne may have wished: too exotic. Just the accent, though, if muted, can have an appealing otherness. The second index of the acceptability of the non-American, again Australian, has yet to be tested on the American market place. Called Paradise Beach, it is not a ready-made Australian soap seeking overseas sales, but a co-production between the Australian-based Village Roadshow, Australia’s Channel 9, and the American New World Entertainment, which has secured pre-sales to the CBS network at 7:30 p.m. week-nights (beginning June 14, 1993) and Britain’s Sky Channel as well as in nine other territories worldwide (Gill 1993; Chester 1993; Shohet 1993). As an Australian-based soap directed primarily at a teen audience, it recalls Neighbours and Home and Away. As a youth drama serial set in a beach tourism center, it recalls Baywatch and summer holiday editions of Beverly Hills 90210. And like Melrose Place and the Australian E Street, each episode includes what one report breathily calls “an MTV moment . . . a two-minute montage of sleek shots of beautiful bodies and plenty of sun, surf and sand set to the latest pop music hit” (Shohet 1993: 5). Set in and around Surfers Paradise on Queensland’s Gold Coast, it recalls, for Australian viewers, the 1983 film, Coolangatta Gold, which celebrates Australian beach culture (see Crofts 1990). It is noteworthy indeed that most of the performers are recuited from a model agency, not an actor’s agency. An American actor, Matt Lattanzi, plays an American photographer, and Australian actor, Tiffany Lamb, sports an American accent. There is a concern, understandable in a program sold overseas, to make Australian colloquialisms comprehensible (Gill 1993: 2). In terms of physical geography, the locations are Australian; in terms of cultural geography, Queensland’s Gold Coast is substantially indistinguishable from much of Florida and parts of California and Hawaii. The era of the co-production re-poses the question of the degree of acceptability of non-American material in the American market-place by begging the question of the distinguishability of the two. But given the unequal cultural exchange long obtaining between Australia and the US, with shows like Mission: Impossible being filmed in Australia to take advantage of cheap labor; given the tight money of Paradise Beach’s shooting schedule of 2.5 hours of soap per week; and given New World’s Head’s, James McNamara, ignorance of Australian soaps (“Paradise Beach is the first soap to be skewed at a teen audience” (quoted by Gill 1993: 2)), one might wonder which party is defining the." In To Be Continued..., 123. Routledge, 2002. http://dx.doi.org/10.4324/9780203131855-25.

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Тези доповідей конференцій з теми "Network co-production model":

Khan, Hassan, and Clifford Louis. "An Artificial Intelligence Neural Networks Driven Approach to Forecast Production in Unconventional Reservoirs – Comparative Analysis with Decline Curve." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21350-ms.

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Abstract Subsurface engineers pivot on surveillance of reservoir performance for future production rates and plan the optimization strategies at earliest. There are some techniques preferred for unconventional reservoirs such as numerical simulation and decline curve analysis (DCA) for production forecasting, but the uncertainty of uneconomical well test data often occurs in unconventional resources. Moreover, reservoir engineers can also hit a tailback in optimizing and tuning the model. Further, for DCA this approach is only appropriate for well/reservoir that are under boundary dominant flow regime, whereas fracture dominant flow regime is often observed for a longer period in unconventional hydraulically fractured reservoirs. Therefore, to resolve this issue, oil & gas industry (O&G) can adopt AI (Artificial Intelligence) based Algorithms for production forecasting. This paper presents a data-driven algorithm, known as Artificial Neural Networks (ANNs), along with time series forecasting that is a well-known statistical technique. Machine learning model trained by a past well performance data such as tubing head pressure (THP), flowing bottom-hole pressure can predict future production rates. This can be an efficient technique for subsurface engineers to monitor and optimize well performance. Time series neural networks were used for training the model at top and bottom node of the well with variating pressures in the past. After training and validation, the model predicted a target parameter that was gas rate. ANNs are inspired by biological neurons that are present in human brain, a powerful computing tool to make decisions after fueling itself with data. Moreover, prediction (t+1) nonlinear automated regression is preferred for accurate step ahead. Production rates and constraints of unconventional reservoirs were used to train the model. In our results, the NN based model gave the co-efficient of determination (R2) of 0.996 that shows nearly an exact precision. Furthermore, the values generated from NN Model and Arp's decline curve calculations were plotted for validation and it turned out that ANN can accurately predict the parameters. The Neural Network model is a novel approach for production forecasting, of unconventional reservoirs and help engineers in corporate decision making. This approach can mitigate the need of uneconomical well test operations and further provide confidence to production engineers in terms of data and result expectations.

Hackney, R., S. K. Sadasivuni, J. W. Rogerson, and G. Bulat. "Predictive Emissions Monitoring System for Small Siemens Dry Low Emissions Combustors: Validation and Application." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-57656.

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Predictions of the Siemens 1D reactor network model, GENE-AC, are validated against emissions measurements from high pressure tests of an industrial gas turbine combustor. The same modelling approach is then applied to the Siemens SGT-400 production engine, where trends predicted by a reactor network model of the SGT-400 combustor are used as the basis for a Predictive Emissions Monitoring System. Input parameters include: load, pilot/main fuel split, equivalence ratio, and inlet air temperature and pressure. Comparison with a sample of 90 engine tests demonstrates reasonably accurate prediction of NOx and CO without tuning the model to match individual engines.

Ubando, Aristotle T., Joel L. Cuello, Mahmoud M. El-Halwagi, Alvin B. Culaba, and Raymond R. Tan. "Multi-Regional Multi-Objective Optimization of an Algal Biofuel Polygeneration Supply Chain With Fuzzy Mathematical Programming." In ASME 2014 8th International Conference on Energy Sustainability collocated with the ASME 2014 12th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/es2014-6461.

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A polygeneration approach is proposed to improve the economic viability of algal biofuel production through simultaneous production of co-products (i.e. electricity, heat, and other biochemicals). A multi-regional polygeneration supply chain consists of various array of processing plants in producing multiple bioenergy products given spatial constraints of each plant found in different regions. The inherent complexity of the polygeneration compounds the difficulty of designing the composite network of processing plants in multi-regions. Optimizing the design flow of the polygeneration supply chain considers multiple objectives, such as satisfying product demand, maximizing economic performance, and minimizing environmental footprint. In addition, the optimal strategic capacity design of the supply and distribution of biodiesel across multi-regions are considered. This study uses a fuzzy mathematical programming model to generate an optimized design of the polygeneration supply chain while satisfying all objectives. The developed model is demonstrated using a modified industrial case study comparing two cultivation alternatives. Results showed that all fuzzy multi-objective goals are satisfied and the flat-plate photobioreactor is the preferred cultivation system in terms of environmental footprints and economic performance.

Pundle, Anamol, David G. Nicol, Philip C. Malte, and Joel D. Hiltner. "Modeling the Formation of Pollutant Emissions in Large-Bore, Lean-Burn Gas Engines." In ASME 2017 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icef2017-3577.

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This paper discusses chemical kinetic modeling used to analyze the formation of pollutant emissions in large-bore, lean-burn gas reciprocating engines. Pollutants considered are NOx, CO, HCHO, and UHC. A quasi-dimensional model, built as a chemical reactor network (CRN), is described. In this model, the flame front is treated as a perfectly stirred reactor (PSR) followed by a plug flow reactor (PFR), and reaction in the burnt gas is modeled assuming a batch reactor of constant-pressure and fixed-mass for each crank angle increment. The model treats full chemical kinetics. Engine heat loss is treated by incorporating the Woschni model into the CRN. The mass burn rate is selected so that the modeled cylinder pressure matches the experiment pressure trace. Originally, the model was developed for large, low speed, two-stoke, lean-burn engines. However, recently, the model has been formatted for the four-stroke, open-chamber, lean-burn engine. The focus of this paper is the application of the model to a four-stroke engine. This is a single-cylinder non-production variant of a heavy duty lean-burn engine of about 5 liters cylinder displacement Engine speed is 1500 RPM. Key findings of this work are the following. 1) Modeled NOx and CO are found to agree closely with emission measurements for this engine over a range of relative air-fuel ratios tested. 2) This modeling shows the importance of including N2O chemistry in the NOx calculation. For λ = 1.7, the model indicates that about 30% of the NOx emitted is formed by the N2O mechanism, with the balance from the Zeldovich mechanism. 3) The modeling shows that the CO and HCHO emissions arise from partial oxidation late in the expansion stroke as unburned charge remaining mixes into the burnt gas. 4) Model generated plots of HCHO versus CH4 emission for the four-stroke engine are in agreement with field data for large-bore, lean-burn, gas reciprocating engines. Also, recent engine tests show the correlation of UHC and CO emissions to crevice volume. These tests suggest that HCHO emissions also are affected by crevice flows through partial oxidation of UHC late in the expansion stroke.

Hemmes, Kas, Anish Patil, and Nico Woudstra. "Internal Reforming SOFC System for Flexible Coproduction of Hydrogen and Power." In ASME 2005 3rd International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2005. http://dx.doi.org/10.1115/fuelcell2005-74153.

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In the framework of the project Greening of Gas, in which the feasibility of mixing hydrogen into the natural gas network in the NL is studied, we are exploring alternative hydrogen production methods. Fuel cells are usually only seen as devices that convert hydrogen into power and heat. It is less well known that these electrochemical energy converters can produce hydrogen, or form an essential component in systems for co-production of hydrogen and power. Co-production of hydrogen and power from NG in an Internal reforming fuel cell (IR FC) is worked out by flow sheet calculations on an Internal reforming Solid Oxide fuel cell (IR-SOFC) system. It is shown that the system can operate in a wide range of fuel utilization values at least from 60% representing highest hydrogen production mode to 95% corresponding to ‘normal’ fuel cell operation mode. For the atmospheric pressure system studied here hydrogen and CO content increase up to 22.6 and 13.5 % respectively at a fuel utilization of 60%. Total system efficiency (power + H2/CO) is increasing significantly at lower fuel utilization and can reach 94 %. Our study confirms that the calculations of Vollmar et al1) on an IR-SOFC stack also hold for a complete FC system. Notably that paradoxically a system with the same fuel cell stack when switched to hydrogen production mode can yield more power in addition to the H2 and CO produced. This is because the hydrogen production mode allows for operation at high current and power densities. The same system can double its power output (e.g. from 1.26 MW to 2.5 MW) while simultaneously increasing the H2 /CO output to 3.1MW). Economics of these systems is greatly improved. These systems can also be considered for hydrogen production for the purpose of mixing it with natural gas in the natural gas grid in order to reduce CO2 emissions at the end users, because of the ability to adopt the system rapidly to fluctuations in natural gas/hydrogen demand.

Fackler, K. Boyd, Megan F. Karalus, Igor V. Novosselov, John C. Kramlich, and Phillip C. Malte. "Experimental and Numerical Study of NOX Formation From the Lean Premixed Combustion of CH4 Mixed With CO2 and N2." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-45090.

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This paper describes an experimental and numerical study of the emission of nitrogen oxides (NOX) from the lean premixed (LPM) combustion of gaseous fuel alternatives to typical pipeline natural gas in a high intensity, single-jet stirred reactor (JSR). In this study, CH4 is mixed with varying levels CO2 and N2. NOX measurements are taken at a nominal combustion temperature of 1800 K, atmospheric pressure, and a reactor residence time of 3 ms. The experimental results show the following trends for NOX emissions as a function of fuel dilution: (1) more NOX is produced per kg of CH4 consumed with the addition of a diluent, (2) the degree of increase in emission index is dependent on the chosen diluent; N2 dilution increases NOX production more effectively than equivalent CO2 dilution. Chemical kinetic modelling suggests that NOX production is less effective for the mixture diluted with CO2 due to both a decrease in N2 concentration and the ability of CO2 to deplete the radicals taking part in NOX formation chemistry. In order to gain insight on flame structure within the JSR, three dimensional computational fluid dynamic (CFD) simulations are carried out for LPM CH4 combustion. A global CH4 combustion mechanism is used to model the chemistry. While it does not predict intermediate radicals, it does predict CH4 and CO oxidation quite well. The CFD model illustrates the flow-field, temperature variation, and flame structure within the JSR. A 3-element chemical reactor network (CRN), including detailed chemistry, is constructed using insight from detailed spatial measurements of the reactor, the results of CFD simulations, and classical fluid dynamic correlations. GRI 3.0 is used in the CRN to model the NOX emissions for all fuel blends. The experimental and modelling results are in good agreement and suggest the underlying chemical kinetic reasons for the trends.

Sasso, Maurizio, Raffaello Possidente, Carlo Roselli, and Sibilio Sergio. "3-E Analysis of a Heat Pump Driven by a Micro-Cogenerator." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80885.

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The cogeneration, or the combined production of electric (and/or mechanical) and thermal energy, is a well established technology, which has important environmental benefits and it has been noted by the European Community as one of the first elements to save primary energy, to avoid network losses and to reduce the greenhouse gas emissions. In particular, the study will be focused on the micro-cogeneration process with micro-combined heat and power system, or MCHP (electric power output ≤ 15 kW), which represents a valid and interesting application of this technology applicable, above all, to residential and light commercial users. This paper presents the Energy, Economic and Environmental (3-E) analysis of a natural gas-fired MCHP in combination with an electric heat pump (EHP). The 3-E analysis of the MCHP/EHP begins with the results of a detailed experimental activity developed in a test facility [1] for a wide range of conditions. Two operating conditions were simulated: a heating mode with co-production of electric and thermal energy, and a cooling mode with co-production of electric, thermal and cooling energy (tri-generation). The annual operating performance, also based on the typical features of the Italian market, is also discussed with a simplified approach.

Martell, Meritxell, and Anne Bergmans. "Potential Scenarios for Broadening Stakeholder Involvement in the Implementing Geological Disposal Technology Platform." In ASME 2013 15th International Conference on Environmental Remediation and Radioactive Waste Management. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icem2013-96151.

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This paper analyses the potential for the involvement of different types of stakeholders in the Implementing Geological Disposal Technology Platform (IGD-TP). This analysis was conducted as part of the InSOTEC project, a three-year (2011–2014) collaborative research project funded under the 7th Euratom Framework Programme (Grant Agreement nr. 269906). In our analysis, we consider the extent to which the IGD-TP’s practice as regards to stakeholder involvement matches its discourse, and what potential for improvement exists given its structural organisation as a European Technology Platform (ETPs). Technology Platforms (TPs) can be understood as knowledge networks, deliberately set up to influence (research) policy in a specific domain. We therefore use knowledge networks as a conceptual approach and look at the IGD-TP as a complex network which includes actors, knowledge and practices across different countries, focusing on a very specific topic (i.e. implementing geological disposal). We compare the way different stakeholders are involved in the IGD-TP to the practice of other ETPs, and explore how the IGD-TP is viewed by its members and by outsiders to the platform. Applying Callon’s framework of knowledge co-production (1999) we come to define different degrees of interaction between science, society and policy in view of defining research and development (R&D) priorities [1]. Subsequently we describe how these interactions could be conceptualised and interpreted for the IGD-TP. The current approach of the IGD-TP can be mainly understood as classical model involving mainly expert stakeholders and scientists. Where there seems to be a good representation among IGD-TP members of industry, research institutes, and some members of the academic community this is not the case for other types of stakeholders, such as public authorities or civil society. At this stage, the overall approach of the IGD-TP would seem to restrict the scope of stakeholder involvement, as it narrows participation down to uniquely technology experts, hindering socio-technical manifestations. Our analysis nevertheless shows that there is room for engaging with a broader range of stakeholders in the field of radioactive waste management, if this is the intention of the IGD-TP. However, this would require a commitment to developing a common knowledge base including other stakeholders through a process of mutual adjustment and negotiation.

Lipperheide, Moritz, Thomas Bexten, Manfred Wirsum, Martin Gassner, and Stefano Bernero. "A Comparative Study of Data and Physically Based Gas Turbine Modeling for Long-Term Monitoring Scenarios: Part II — Emission Prediction Utilizing Different Levels of Design Information." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76650.

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Reliable engine and emission models allow for an online monitoring of commercial gas turbine operation and help the plant operator and the original equipment manufacturer (OEM) to ensure emission compliance of the aging engine. However, model development and validation require fine-tuning on the particular engines, which may differ in a fleet of a single design type by production, assembly and aging status. For this purpose, Artificial Neural Networks (ANN) offer a good and fast alternative to traditional physically-based engine modeling, because the model creation and adaption is merely an automatized process in commercially available software environments. However, ANN performance depends strongly on the availability of suitable data and a-priori data processing. The present work investigates the impact of specific engine information from the OEM’s design tools on ANN performance. As an alternative to a strictly data-based benchmark approach, engine characteristics were incorporated into ANNs by a pre-processing of the raw measurements with a simplified engine model. The resulting ‘virtual’ measurements, i.e. hot gas temperatures, then served as inputs to ANN training and application during long-term gas turbine operation. When processed input parameters were used for ANNs, overall long-term NOx prediction improved by 55%, and CO prediction by 16% in terms of RMSE, yielding comparable overall RMSE values to the physically-based model.

Colella, Whitney G. "Optimizal Design and Control Strategies for Novel Combined Heat and Power (CHP) Fuel Cell Systems: Part II of II—Case Study Results." In ASME 2010 8th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2010. http://dx.doi.org/10.1115/fuelcell2010-33147.

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Innovative energy system optimization models are deployed to evaluate novel fuel cell system (FCS) operating strategies, not typically pursued by commercial industry. Most FCS today are installed according to a “business-as-usual” approach: 1) stand-alone (unconnected to district heating networks and low-voltage electricity distribution lines), 2) not load following (not producing output equivalent to the instantaneous electrical or thermal demand of surrounding buildings), 3) employing a fairly fixed heat-to-power ratio (producing heat and electricity in a relatively constant ratio to each other), and 4) producing only electricity and no recoverable heat. By contrast, models discussed here consider novel approaches as well. Novel approaches include 1) networking (connecting FCSs to electrical and/or thermal networks), 2) load following (having FCSs produce only the instantaneous electricity or heat demanded by surrounding buildings), 3) employing a variable heat-to-power ratio (such that FCS can vary the ratio of heat and electricity they produce), 4) co-generation (combining the production of electricity and recoverable heat), 5) permutations of these together, and 6) permutations of these combined with more “business-as-usual” approaches. (The detailed assumptions and methods behind these models are described in Part I of this article pair. Reprinted from Part 1, TABLE 1 below summarizes twelve novel operating strategies investigated by the models.