NASA Thesaurus Supplement: A three part cumulative supplement to the 1982 edition of the NASA Thesaurus (supplement 2)

The three part cumulative NASA Thesaurus Supplement to the 1982 edition of the NASA Thesaurus includes: part 1, hierarchical listing; part 2, access vocabulary, and part 3, deletions. The semiannual supplement gives complete hierarchies for new terms and includes new term indications for terms new to this supplement

Supercritical CO2 extraction of Tetraclinis articulata: chemical composition, antioxidant activity and mathematical modeling

Operating conditions for extraction from the leaves of Tetraclinis articulata using supercritical carbon diox-ide (SCCO2) were studied to focus on the feasibility of obtaining volatile and nonvolatile fractions throughthe use of different extraction pressures (90, 280 and 1000 bar). In addition, influence of temperature,static pretreatment and dynamic extraction durations, particle size and CO2flow rate were investigated.All extracts were analyzed by GC–FID/MS and their antioxidant activity was measured using ABTS•+andDPPH•methods. Conventional hydrodistillation (HD) was also performed for comparison. At high CO2pressure (280 and 1000 bar), the amount of phenolics in the extracts was higher (respectively 102.03and 267.90 GAE mg/g) than for HD and supercritical fluid extraction (SFE) at 90 bar (respectively 8.89 and9.70 GAE mg/g). Correlatively, high antioxidant activity was found for high pressure SFE. Surprisingly, forextracts obtained by SFE at 90 bar, despite very low phenolic content, significant antioxidant activity wasobserved, while essential oil obtained by HD, which presented also low phenolic content, exhibited lowantioxidant activity.Physical aspects were only investigated for the low pressure supercritical extraction (90 bar) process.Qualitative assessment of kinetic curves together with their modeling revealed that the extraction pro-cess was mainly limited by the thermodynamic equilibrium of easily accessible solutes but where axialdispersion was significant. From this result a simple extrapolation procedure was proposed

Experimental and modeling studies on microwave-assisted extraction of mangiferin from Curcuma amada

Mangiferin, a bioactive compound having potent nutraceutical, strong antioxidant and pharmacological significance has been extracted using microwave-assisted extraction (MAE) technique from Curcuma amada, commonly known as mango ginger. The extraction solvent ethanol is eco-friendly, nontoxic and reduces the risk of environmental hazards. The influence of several independent variables such as microwave power, ethanol concentration, extraction (irradiation) time and pre-leaching time has been studied under varying conditions using one-factor-at-a-time analysis to obtain an optimal extraction ratio. The maximum mangiferin content of 1.1156 mg/g is obtained at microwave power of 550 W and extraction time of 50 s with 80 % ethanol as a solvent and pre-leaching time of 20 min. The results indicate that microwave power and ethanol concentration have the most significant effect on the yield of mangiferin content. The presence of mangiferin in final Curcuma amada extract is confirmed through high-performance liquid chromatography and the functional groups are identified through Fourier transform infrared spectroscopy analyses using standard mangiferin. The experimental profiles are fitted into a two-parameter modified first-order kinetic model and a three-parameter modified logistic model and checked using the goodness-of-fit criterion. The Curcuma amada retained its antioxidant activity after MAE treatment and the antioxidant activity of mangiferin obtained after extraction using DPPH free radical scavenging assay is studied

NASA Thesaurus Supplement: A three part cumulative supplement to the 1982 edition of the NASA Thesaurus (supplement 3)

The three part cumulative NASA Thesaurus Supplement to the 1982 edition of the NASA Thesaurus includes Part 1, Hierarchical Listing, Part 2, Access Vocabulary, and Part 3, Deletions. The semiannual supplement gives complete hierarchies for new terms and includes new term indications for entries new to this supplement

Modeling and Control of Post-Combustion CO2 Capture Process Integrated with a 550MWe Supercritical Coal-fired Power Plant

This work focuses on the development of both steady-state and dynamic models for an monoethanolamine (MEA)-based CO2 capture process for a commercial-scale supercritical pulverized coal (PC) power plant, using Aspen PlusRTM and Aspen Plus DynamicsRTM. The dynamic model also facilitates the design of controllers for both traditional proportional-integral-derivative (PID) and advanced controllers, such as linear model predictive control (LMPC), nonlinear model predictive control (NMPC) and H? robust control.;A steady-state MEA-based CO2 capture process is developed in Aspen PlusRTM. The key process units, CO2 absorber and stripper columns, are simulated using the rate-based method. The steady-state simulation results are validated using experimental data from a CO2 capture pilot plant. The process parameters are optimized with the goal of minimizing the energy penalty. Subsequently, the optimized rate-based, steady-state model with appropriate modifications, such as the inclusion of the size and metal mass of the equipment, is exported into Aspen Plus DynamicsRTM to study transient characteristics and to design the control system. Since Aspen Plus DynamicsRTM does not support the rate-based model, modifications to the Murphree efficiencies in the columns and a rigorous pressure drop calculation method are implemented in the dynamic model to ensure consistency between the design and off-design results from the steady-state and dynamic models. The results from the steady-state model indicate that between three and six parallel trains of CO2 capture processes are required to capture 90% CO2 from a 550MWe supercritical PC plant depending on the maximum column diameter used and the approach to flooding at the design condition. However, in this work, only two parallel trains of CO2 capture process are modeled and integrated with a 550MWe post-combustion, supercritical PC plant in the dynamic simulation due to the high calculation expense of simulating more than two trains.;In the control studies, the performance of PID-based, LMPC-based, and NMPC-based approaches are evaluated for maintaining the overall CO2 capture rate and the CO2 stripper reboiler temperature at the desired level in the face of typical input and output disturbances in flue gas flow rate and composition as well as change in the power plant load and variable CO2 capture rate. Scenarios considered include cases using different efficiencies to mimic different conditions between parallel trains in real industrial processes. MPC-based approaches are found to provide superior performance compared to a PID-based one. Especially for parallel trains of CO2 capture processes, the advantage of MPC is observed as the overall extent of CO2 capture for the process is maintained by adjusting the extent of capture for each train based on the absorber efficiencies. The NMPC-based approach is preferred since the optimization problem that must be solved for model predictive control of CO2 capture process is highly nonlinear due to tight performance specifications, environmental and safety constraints, and inherent nonlinearity in the chemical process. In addition, model uncertainties are unavoidable in real industrial processes and can affect the plant performance. Therefore, a robust controller is designed for the CO2 capture process based on ?-synthesis with a DK-iteration algorithm. Effects of uncertainties due to measurement noise and model mismatches are evaluated for both the NMPC and robust controller. The simulation results show that the tradeoff between the fast tracking performance of the NMPC and the superior robust performance of the robust controller must be considered while designing the control system for the CO2 capture units. Different flooding control strategies for the situation when the flue gas flow rate increases are also covered in this work

Identification and energy optimization of supercritical carbon dioxide batch extraction

Abstract. The emergence of green chemistry, aiming to increase ecological and energy efficiency of processes, has gained supercritical fluid extraction increasing amounts of prominence. Traditional extraction methods utilize hazardous chemicals, have low extractive yield in relation to energy consumption, and produce large amounts of organic waste. Supercritical fluid extraction offers improvements to these challenges in the form of reduced processing energy inputs and an alternative solvent approach. Carbon dioxide is the most commonly employed solvent in supercritical fluid extraction due to the many advantages it brings over other solvents including price, smaller environmental and health risks, and simple separation. The research on data-driven system identification and advanced process control of supercritical extraction has been very scarce. According to past research, the control of supercritical is mostly carried out using basic, non-model-based control schemes. Challenges such as coupling between control loops and nonlinearities of fluid and process dynamics create major challenges for the basic control schemes. With advanced control methods, it could be possible to address these challenges better. Model-based control schemes, in theory, pose many advantages and benefits over basic control, such as improved production economics, optimized product quality and yields, and further possibilities in model-driven research and development. The goal of this thesis was to improve control performance and optimize energy consumption a pilot-scale batch supercritical carbon dioxide extraction process by utilizing model predictive control strategies. The modeling of the unit processes of the target batch extraction was based on measurement data gathered by experimental design and careful examination of the system. The models were utilized in a simulator developed in this study. The arrangement of the implemented experimental design (central composite design, CCD) allowed the exploitation of linear regression analysis; the results of which indicated the existence of possible nonlinearities between steady-state electricity consumption and the operative variables of the process. Model predictive control schemes were developed in a simulator environment for carbon dioxide pressure control, carbon dioxide volumetric flow control, extractor temperature control and separator temperature control. The developed control schemes showed major improvements in control performance of the simulated unit processes, resulting in significant decreases in total electricity and heating water consumptions (up to 25% and 21% respectively). Model predictive control also proved to be quite flexible over the base control system for some processes, providing the possibility of modifying control performance by simple tuning adjustments. The simulated control strategies demonstrate the benefits of model-based control in terms of process energy efficiency and economy. In addition to these results, the identified process and controller models have further potential in future research on control and process developments of supercritical fluid extraction.Ylikriittisen hiilidioksidipanosuuton identifiointi ja energiaoptimointi. Tiivistelmä. Prosessien ekologisuuden ja energiatehokkuuden lisäämiseen tähtäävä vihreä kemia edistää ylikriittisen uuton merkittävyyttä yhä enemmän. Perinteiset erotusmenetelmät käyttävät haitallisia kemikaaleja, niillä on alhainen uuteainesaanto suhteessa energian kulutukseen, ja ne tuottavat suuren määrän orgaanista jätettä. Ylikriittinen uutto tarjoaa parannuksia näihin haasteisiin prosessointienergian kulutuksen vähentymisen ja vaihtoehtoisen liuotinratkaisun muodossa. Hiilidioksidi on yleisimmin käytetty liuotin ylikriittisessä uutossa, koska sillä on monia etuja muihin liuottimiin verrattuna, mukaan lukien hinta, pienemmät ympäristö- ja terveysriskit sekä yksinkertainen erottaminen. Ylikriittiseen uuttoprosessiin liittyvän datapohjaisen identifioinnin ja kehittyneen säädön tutkimus on ollut hyvin vähäistä. Aiempien tutkimusten perusteella ylikriittisen uuton säätö toteutetaan pääasiassa perustason ei-mallipohjaisilla säätörakenteilla. Ohjaussilmukoiden vuorovaikutukset sekä neste- ja prosessidynamiikan epälineaarisuudet luovat suuria haasteita perussäätörakenteille. Kehittyneillä säätömenetelmillä olisi mahdollista käsitellä näitä haasteita paremmin. Mallipohjaiset säätöratkaisut tuovat teoriassa useita etuja ja hyötyjä perussäätöön verrattuna parantuvan tuotantoekonomian, optimoidun tuotelaadun ja -saannon sekä malliperusteisen tutkimuksen ja -kehityksen lisämahdollisuuksien muodossa. Tämän työn tavoitteena oli nostaa pilottikoon ylikriittisen hiilidioksidipanosuuttoprosessin säädön suorituskykyä ja optimoida energiankulutusta hyödyntämällä mallipredikriivisiä säätöstrategioita. Tutkimuksen kohteena olleen panosuuton yksikköprosessien mallinnus perustui koesuunnittelulla kerättyyn mittausaineistoon ja järjestelmän huolelliseen tarkkailuun. Malleja hyödynnettiin työssä kehitetyssä prosessisimulaattorissa. Toteutettu koessunnitelma (central composite design, CCD) mahdollisti lineaarisen regressioanalyysin hyödyntämisen, jonka tulokset osoittivat mahdollisten epälineaarisuuksien olemassaolon prosessin vakaan tilan sähkönkulutuksen ja operatiivisten muuttujien välillä. Malliprediktiiviset säätörakenteet kehitettiin simulaatioympäristössä hiilidioksidin paineen, hiilidioksidin tilavuusvirtauksen, uuttoreaktorin lämpötilan, ja erottajan lämpötilan säädöille. Kehitetyt säätörakenteet toivat suuria säätöparannuksia simuloituihin yksikköprosesseihin, johtaen merkittäviin vähennyksiin käyttösähkön- ja lämmitysveden kulutuksissa (vastaavat vähennykset 25 % ja 21 % saakka). Malliprediktiivinen säätö osoitti myös joustavuutensa perusäätöjärjestelmään verrattuna joissakin prosesseissa, mahdollistaen säätösuorituskyvyn modifioinnin yksinkertaisilla viritysmuutoksilla. Simuloidut säätöstrategiat havainnollistavat mallipohjaisen säädön mahdollisia hyötyjä prosessin energiatehokkuuden ja taloudellisuuden kannalta. Näiden tulosten lisäksi identifioiduilla prosessi- ja säädinmalleilla on lisäpotentiaalia tulevaisuuden ylikriittisen uuton säädön tutkimuksissa ja prosessikehityksissä

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Artificial Intelligence driven smart operation of large industrial complexes supporting the net-zero goal: Coal power plants

The true potential of artificial intelligence (AI) is to contribute towards the performance enhancement and informed decision making for the operation of the large industrial complexes like coal power plants. In this paper, AI based modelling and optimization framework is developed and deployed for the smart and efficient operation of a 660 MW supercritical coal power plant. The industrial data under various power generation capacity of the plant is collected, visualized, processed and subsequently, utilized to train artificial neural network (ANN) model for predicting the power generation. The ANN model presents good predictability and generalization performance in external validation test with R2 = 0.99 and RMSE =2.69 MW. The partial derivative of the ANN model is taken with respect to the input variable to evaluate the variable’ sensitivity on the power generation. It is found that main steam flow rate is the most significant variable having percentage significance value of 75.3 %. Nonlinear programming (NLP) technique is applied to maximize the power generation. The NLP-simulated optimized values of the input variables are verified on the power generation operation. The plant-level performance indicators are improved under optimum operating mode of power generation: savings in fuel consumption (3 t/h), improvement in thermal efficiency (1.3 %) and reduction in emissions discharge (50.5 kt/y). It is also investigated that maximum power production capacity of the plant is reduced from 660 MW to 635 MW when the emissions discharge limit is changed from 510 t/h to 470 t/h. It is concluded that the improved plant-level performance indicators and informed decision making present the potential of AI based modelling and optimization analysis to reliably contribute to net-zero goal from the coal power plant

Comparison of different methods for extraction from Tetraclinis articulata: Yield, chemical composition and antioxidant activity

In the present study, three techniques of extraction: hydrodistillation (HD), solvent extraction (conventional ‘Soxhlet’ technique) and an innovative technique, i.e., the supercritical fluid extraction (SFE), were applied to ground Tetraclinis articulata leaves and compared for extraction duration, extraction yield, and chemical composition of the extracts as well as their antioxidant activities. The extracts were analyzed by GC–FID and GC–MS. The antioxidant activity was measured using two methods: ABTS and DPPH. The yield obtained using HD, SFE, hexane and ethanol Soxhlet extractions were found to be 0.6, 1.6, 40.4 and 21.2–27.4 g/kg respectively. An original result of this study is that the best antioxidant activity was obtained with an SFE extract (41 mg/L). The SFE method offers some noteworthy advantages over traditional alternatives, such as shorter extraction times, low environmental impact, and a clean, non-thermally-degraded final product. Also, a good correlation between the phenolic contents and the antioxidant activity was observed with extracts obtained by SFE at 9 MPa