The whole-process simulation software development based on M language

2011 ◽  
Author(s):  
Zhu Wen-wen ◽  
Huang Yi-min
Keyword(s):  
Software Development ◽  
Process Simulation ◽  
Simulation Software ◽  
Whole Process

Modeling of a Steam Turbine Including Partial Arc Admission for Use in a Process Simulation Software Environment

2011 ◽  
Author(s):  
Eric Liese
Keyword(s):  
Steam Turbine ◽  
Process Simulation ◽  
Process Model ◽  
Constant Pressure ◽  
Pressure Ratio ◽  
Simulation Software ◽  
State Variables ◽  
Steam Turbines ◽  
Software Environment ◽  
Last Stage

A dynamic process model of a steam turbine, including partial arc admission operation, is presented. Models were made for the first stage and last stage, with the middle stages presently assumed to have a constant pressure ratio and efficiency. A condenser model is also presented. The paper discusses the function and importance of the steam turbines entrance design and the first stage. The results for steam turbines with a partial arc entrance are shown, and compare well with experimental data available in the literature, in particular, the “valve loop” behavior as the steam flow rate is reduced. This is important to model correctly since it significantly influences the downstream state variables of the steam, and thus the characteristic of the entire steam turbine, e.g., state conditions at extractions, overall turbine flow, and condenser behavior. The importance of the last stage (the stage just upstream of the condenser) in determining the overall flowrate and exhaust conditions to the condenser is described and shown via results.

Program Comprehension through Data Mining

2011 ◽  
pp. 1603-1609
Author(s):  
Ioannis N. Kouris
Keyword(s):  
Data Mining ◽  
Software Development ◽  
Software Maintenance ◽  
Real Life ◽  
Program Comprehension ◽  
Development Phase ◽  
Code Length ◽  
Production Phase ◽  
Whole Process ◽  
Two Phases

Software development has various stages, that can be conceptually grouped into two phases namely development and production (Figure 1). The development phase includes requirements engineering, architecting, design, implementation and testing. The production phase on the other hand includes the actual deployment of the end product and its maintenance. Software maintenance is the last and most difficult stage in the software lifecycle (Sommerville, 2001), as well as the most costly one. According to Zelkowitz, Shaw and Gannon (1979) the production phase accounts for 67% of the costs of the whole process, whereas according to Van Vliet (2000) the actual cost of software maintenance has been estimated at more than half of the total software development cost. The development phase is critical in order to facilitate efficient and simple software maintenance. The earlier stages should be done by taking into consideration apart from any functional requirements also the later maintenance task. For example the design stage should plan the structure in a way that can be easily altered. Similarly, the implementation stage should create code that can be easily read, understood, and changed, and should also keep the code length to a minimum. According to Van Vliet (2000) the final source code length generated is the determinant factor for the total cost during maintenance, since obviously the less code is written the easier the maintenance becomes. According to Erdil et al. (2003) there are four major problems that can slow down the whole maintenance process: unstructured code, maintenance programmers having insufficient knowledge of the system, documentation being absent, out of date, or at best insufficient, and software maintenance having a bad image. Thus the success of the maintenance phase relies on these problems being fixed earlier in the life cycle. In real life however when programmers decide to perform some maintenance task on a program such as to fix bugs, to make modifications, to create software updates etc. these are usually done in a state of time and commercial pressures and with the logic of cost reduction, thus finally resulting in a problematic system with ever increased complexity. As a consequence the maintainers spend from 50% up to almost 90% of their time trying to comprehend the program (Erdös and Sneed; 1998, Von Mayrhauser and Vans; 1994, Pigoski, 1996). Providing maintainers with tools and techniques to comprehend the programs has become and is receiving a lot of financial and research interest given the widespread of computers and software in all aspects of life. In this work we briefly present some of the most important techniques proposed in the field thus far and focus primarily on the use of data mining techniques in general and especially on association rules. Accordingly we give some possible solutions to problems faced by these methods.

Safety and Efficiency Enhancement in LNG Terminals

2017 ◽  
pp. 1584-1596
Author(s):  
Ravinder Singh ◽  
Helen Huiru Lou
Keyword(s):  
High Risk ◽  
Energy Density ◽  
Natural Gas ◽  
Process Simulation ◽  
Simulation Software ◽  
Liquefied Natural Gas ◽  
Efficiency Enhancement ◽  
Plant Operation ◽  
Aspen Hysys ◽  
Safety Enhancement

Liquefaction of natural gas helps in transporting it over long distances by sea vessels. It is then regasified and transported through pipelines to the consumer. Due to large energy density of Liquefied Natural Gas (LNG), and associated flammability issues, the LNG terminal involves high risk. Consequently, safety is an important factor in the operation of LNG terminals. Although a substantial amount of time money and effort has been put in this area, there is always some possibility of improving the process so that less risk is involved. Rapid advancement in process simulation software like Aspen Plus and Aspen HYSYS, has led to the convenience of experimenting the various control methodologies on the computer offline from the actual plant operation, before they are implemented in real time. In this chapter, main hazards associated with LNG terminal operation will be highlighted. Further, recent advancements in research for safety enhancement and efficiency enhancement in the liquefaction and regasification processes will also be included.

scholarly journals Valorization of OFMSW Digestate-Derived Syngas toward Methanol, Hydrogen, or Electricity: Process Simulation and Carbon Footprint Calculation

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 526 ◽  
Author(s):  
Aristide Giuliano ◽  
Enrico Catizzone ◽  
Cesare Freda ◽  
Giacinto Cornacchia
Keyword(s):  
Hydrogen Production ◽  
Process Simulation ◽  
Carbon Capture ◽  
Combustion Engine ◽  
Co2 Storage ◽  
Carbon Capture And Storage ◽  
Simulation Software ◽  
Added Value ◽  
Electricity Production ◽  
Methanol Production

This paper explores a possible waste-based economy transition strategy. Digestate from the organic fraction of municipal solid waste (OFMSW) is considered, as well as a low-added value product to be properly valorized. In this regard, air gasification may be used to produce syngas. In this work, the production of methanol, hydrogen, or electricity from digestate-derived syngas was assessed by ChemCAD process simulation software. The process scheme of methanol production comprises the following parts: water gas shift (WGS) with carbon capture and storage units (CCS), methanol synthesis, and methanol purification. In the case of hydrogen production, after WGS-CCS, hydrogen was purified from residual nitrogen by pressure swing absorption (PSA). Finally, for electricity production, the digestate-derived syngas was used as fuel in an internal combustion engine. The main objective of this work is to compare the proposed scenarios in terms of CO2 emission intensity and the effect of CO2 storage. In particular, CCS units were used for methanol or hydrogen production with the aim of obtaining high equilibrium yield toward these products. On the basis of 100 kt/year of digestate, results show that the global CO2 savings were 80, 71, and 69 ktCO2eq/year for electricity, methanol, and hydrogen production, respectively. If carbon storage was considered, savings of about 105 and 99 ktCO2eq/year were achieved with methanol and hydrogen production, respectively. The proposed scenarios may provide an attractive option for transitioning into methanol or hydrogen economy of the future.

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