Numerical schemes for process simulation: software for coupling pattern recognition to process modelling

Author(s):  
Wouter Zijl ◽  
Anna Trykozko
Author(s):  
Eric Liese

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.


2017 ◽  
pp. 1584-1596
Author(s):  
Ravinder Singh ◽  
Helen Huiru Lou

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.


Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 526 ◽  
Author(s):  
Aristide Giuliano ◽  
Enrico Catizzone ◽  
Cesare Freda ◽  
Giacinto Cornacchia

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.


Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 847 ◽  
Author(s):  
Ellen Argo ◽  
Deepak R. Keshwani

Fed-batch enzymatic hydrolysis has the potential to improve the overall process of converting cellulosic biomass into ethanol. This paper utilizes a process simulation approach to identify and quantify techno-economic differences between batch and fed-batch enzymatic hydrolysis in cellulosic ethanol production. The entire process of converting corn stover into ethanol was simulated using SuperPro Designer simulation software. The analysis was conducted for a plant capacity of 2000 metric tons of dry biomass per day. A literature review was used to identify baseline parameters for the process. The sensitivity of the ethanol production cost to changes in sugar conversion efficiency, plant capacity, biomass cost, power cost, labor cost, and enzyme cost was evaluated using the process simulation. For the base scenario, the ethanol unit production cost was approximately $0.10/gallon lower for fed-batch hydrolysis. The greatest differences were seen in facilities costs, labor costs, and capital costs. Using a fed-batch operation decreased facilities costs by 41%, labor costs by 21%, and capital costs by 15%. The sensitivity analysis found that cost of biomass had the greatest effect on ethanol production cost, and in general, the results support the proposition that fed-batch enzymatic hydrolysis does improve the techno-economics of cellulosic ethanol production.


2014 ◽  
Vol 6 (2) ◽  
pp. 147-150
Author(s):  
Kęstutis Stankevičius ◽  
Olegas Vasilecas

Googling the term “Business Process Simulation” in April 2013 yielded only 42.1 thousand hits. It is not much compared with googling the term such as “Business Process Modelling” in the same time, which yielded approx. 1.470 million hits. That is 35 times more compared to the previous search. The difference between modelling and simulation is arguable. In fact, the terms ‘simulation’ and ‘modelling’ are often used synonymously, but the authors prefer to distinguish between the terms and look at modelling as an act of building a model while simulation is considered an act or even a process of using that model for a specific purpose or study. If simulation is a manipulation process of one or more variables, which can be changed and observed, then this kind of process is best managed and controlled by business rules that can also be manipulated in the simulation process. „Google“ paieškoje įvestas terminas „Business Process Simulation“ 2013 metų balandžio mėn. duoda tik 42,1 tūkst. paieškos rezultatų. Tai nėra daug, palyginti su kitu paieškos terminu „Business Process Modelling“. Tuo pačiu metu reikšminiai paieškos žodžiai duoda apie 1,470 milijono paieškos rezultatų. Tai 35 kartus daugiau, palyginti su prieš tai daryta paieška. Galima ginčytis, ar yra skirtumas tarp modeliavimo ir simuliavimo? Iš tiesų žodžiai „modeliavimas“ ir „simuliavimas“ dažnai vartojami kaip sinonimai, tačiau straipsnyje siūloma į modeliavimą ir simuliavimą žiūrėti skirtingai, t. y. į modeliavimą kaip į veiksmą, kuris sukuria modelį, į simuliavimą – kaip į veiksmą arba procesą, kuris sudaro sąlygas taikyti šį modelį konkrečiam tikslui arba tyrimui atlikti. Jeigu apibrėžiama, kad simuliavimas yra vieno ar daugiau kintamųjų manipuliacijos procesas, kurį galima pakeisti ir stebėti, tada taip pat galima susitarti, kad šis procesas geriausiai grindžiamas verslo taisyklėmis, kurios gali būti papildomos arba keičiamos simuliavimo proceso metu.


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