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The design and retrofit of the heat exchangers in a boiler should take into account the processes occurring on the side of combustion and steam. For this reason, this study aims to couple a one-dimensional hydrodynamic model of steam with computational fluid dynamics (CFD) simulation of flue gas.

Radiant/semi-radiant platen heat exchangers are simplified as plane surfaces for CFD, while convective heat exchangers are introduced into the CFD simulation as energy/momentum absorption sources.

Numerical simulation is performed for a 1,000 MWe coal-fired ultra-supercritical boiler. The calculation results are validated by the thermodynamic design data. Tube outside surface temperature, as well as ash deposit temperature distributions, are obtained.

Complex tube arrangements can be completed with the aid of AutoCAD, and therefore, the simulation could offer detailed information of heat exchangers. In a word, a more reliable modeling of the whole steam generation process is achieved.

The purpose of this paper is to develop and empirically test a conceptual framework to analyze how inter-organizational knowledge sharing facilitates enterprise resource planning (ERP) implementation.

Data were collected from a 2014 survey on 283 Chinese companies. Structural equation modeling was used to test the structural model of the framework.

The results of the study indicated that organizational preparedness (in terms of internal culture, organizational structure, availability of resources, and technological capabilities), positive benefits and costs perception, and external influences (in terms of environmental uncertainty, competitive pressure, and partner readiness) would facilitate inter-organizational knowledge sharing, which in turn, would enhance ERP implementation effectiveness.

Convenience samples from an author’s MBA classes were used in the survey, and a single respondent from each organization answering all questions for such complicated issues also posed the risk of measurement bias and inaccuracy.

The study provided evidences to practitioners about how organizations should collaborate with supply chain partners at a full scale in knowledge generation, maintenance, dissemination, and application in order to enhance their effectiveness on ERP implementation.

ERP projects are predominately observed in existing research as internal initiatives that companies take independently within their own organizational boundaries. This study represents one of the early large-scale empirical efforts to investigate ERP implementation from an inter-firm supply chain collaboration perspective. Additional contribution includes some interesting empirical evidences on the current state of ERP utilization in the Chinese market.

This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data.

First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward.

The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data.

As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders.

The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues.

This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.