Increasing population demand has triggered the enhancement of food production, energy consumption and economic development, however, its impact on climate change has become a global concern. This ...study applied a novel environmental sustainability assessment tool using dynamic Autoregressive-Distributed Lag (ARDL) simulations for model estimation of the relationships between greenhouse gas (GHG) emissions, energy, biomass, food and economic growth for Australia using data spanning from 1970 to 2017. The study found an inversed-U shaped relationship between energy consumption and income level, showing a decarbonized and services economy, hence, improved energy efficiency. While energy consumption increases emissions by 0.4 to 2.8%, biomass consumption supports Australia's transition to a decarbonized economy by reducing GHG emissions by 0.13% and shifts the demand for fossil fuel. Food and energy consumption underpin socio-economic development and vice versa. However, food waste from production and consumption increases ecological footprint, implying a lost opportunity to improve food security and reduce environmental pressure from agricultural production. There is no single path to achieving environmental sustainability, nonetheless, the integrated approach applied in this study reveals conceptual tools which are applicable for decision making.
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•This study assessed the overarching nexus between environment and economic policy.•We employed novel dynamic simulations of Autoregressive-Distributed Lag models.•Evidence shows a shock in food production affects long term energy consumption.•Modernized biomass energy consumption supports the reduction of GHG emissions.•Economic development declines energy intensity and improves energy efficiency.
The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. Robust Predictive Health Monitoring ...tools are needed to guarantee the healthy state of rolling element bearing s during the operation. A Predictive Health Monitoring tool indicates the upcoming failures which provide sufficient lead time for maintenance planning. The Predictive Health Monitoring tool aims to monitor the deterioration i.e. wear evolution rather than just detecting the defects. The Predictive Health Monitoring procedures contain detection, diagnosis and prognosis analysis, which are required to extract the features related to the faulty rolling element bearing and estimate the remaining useful lifetime. The purpose of this study is to review the Predictive Health Monitoring methods and explore their capabilities, advantages and disadvantage in monitoring rolling element bearings. Therefore, the study provides a critical review of the Predictive Health Monitoring methods of the entire defect evolution process i.e. over the whole lifetime and suggests enhancements for rolling element bearing monitoring.
•A review of Predictive health monitoring for rolling bearings is provided.•A review of fault modelling for rolling bearings is provided.•A discussion of wear evolution, surface topology evolution and their influences on the predictive health monitoring process is illustrated.
Vibrations of gear transmission systems (GTSs) are the key problems in the machinery industry. In the previous vibration analysis of GTS, the rotor is usually regarded as a rigid part. The ...rigid-shaft gear-transmission dynamic system RGDS) model cannot solve the differences of vibrations of rotor at different axial positions. To solve this problem, a flexible-shaft gear- transmission system dynamic (FGDS) model is established by using the finite element method. In the FGDS, the GTSs is separated into some flexible shaft segments, gear meshing unit and bearing unit. The bearing stiffness, bearing contact force, time-varying meshing stiffness and damping force of gear are considered. The time- and frequency-domain vibrations of FGDS and RGDS model under different rotational speeds are compared and analyzed. Note that the FGDS can obtain more accurate results than the RGDS model. The vibrations from FGDS are less than those from RGDS model. This paper can give some new approach for the dynamic modeling and vibration analysis of flexible GTSs.
There is an imperative worldwide need to identify effective approaches to deal with water-related risks, and mainly with increasingly frequent floods, as well as with severe droughts. Particularly, ...policy and decision-makers are trying to identify systemic strategies that, going beyond the mere risk reduction, should be capable to deal simultaneously with multiple challenges (such as climate resilience, health and well-being, quality of life), thus providing additional benefits. In this direction, the contribution of Nature Based Solutions (NBS) is relevant, although their wider implementation is still hampered by several barriers, such as the uncertainty and lack of information on their long-term behavior and the difficulty of quantitatively valuing their multidimensional impacts. The activities described in the present paper, carried out within the EU funded project NAIAD, mainly aim at developing a participatory System Dynamic Model capable to quantitatively assess the effectiveness of NBS to deal with flood risks, while producing a multiplicity of co-benefits. The adoption of a participatory approach supported both to increase the available knowledge and the awareness about the potential of NBS and hybrid measures (e.g. a combination of NBS and socio-institutional ones). Specific reference is made to one of the demos of the NAIAD project, namely the Glinščica river case study (Slovenia).
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•Nature-Based Solutions (NBS) reduce water-related risks and produce co-benefits.•Valuing co-benefits is crucial to support NBS mainstreaming.•Participatory activities allow effective stakeholder involvement in NBS co-design.•System Dynamics Modelling (SDM) is used to perform NBS effectiveness assessment.•SDM supports scenario analysis and comparison among different measures.
•The modelling and integration of dynamically operated chemical looping and PSA is presented.•8 beds PSA and 3 beds CLR are required for 130 Nm3/h H2 with > 98% CO2 capture.•Chemical looping ...performance are not affected by the dynamic behavior of the PSA.•Heat losses in chemical looping are mitigated by changing the heat management.
The design of a fully integrated Chemical looping reforming (CLR), single adiabatic water gas shift reactor (WGSR) and Pressure swing adsorption (PSA) operated under dynamic conditions for small scale H2 generation with inherent pure CO2 production is carried out. The dynamically operated packed bed reactors taking part in the chemical looping process have been modelled, designed and simulated to operate with transient feeds from an integrated PSA unit used for the production of 130 Nm3/h of pure H2 (99.9999% purity). As by-product, 51 Nm3/h of pure CO2 (>98.8% purity) is also produced. A rapid cycle 8-bed configuration increases the H2 recovery by 4% whilst reducing the tail gas buffer tank volume requirement by 44%. The effect of the PSA dynamic tail gas composition used as fuel for the CLR reduction reactor stage was found negligible regarding the continuity of the process and the performance of the plant, as it affected only the reduction outlet gas composition profile but had little effect on the reactor bed temperature profile. With respect to the design of the chemical looping reactor beds, an analysis has been performed on the effect of heat losses showing that at higher heat transfer coefficient (U = 5.0 W∙m−2∙K−1) CH4 conversion decreased significantly (≈90% compared to adiabatic operation), therefore a different strategy was implemented. The overall study demonstrates the process design feasibility for producing blue H2 or renewable H2 from methane/bio-methane in decentralised and modular units.
The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of ...contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights – as well as the possibility of controlling the system – may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.
•Structural identifiability and observability are desirable model properties.•They describe a model’s ability to inform about unmeasured parameters and states.•We collect and analyse hundreds of compartmental models of the COVID-19 pandemics.•We show which parameters and states can be determined from output measurements.•We discuss how to choose the most informative model for the available knowledge.
Automatic transdiagnostic risk calculators can improve detection of individuals at risk of psychosis. However, they rely on a single point in time assessment and can be refined with dynamic modelling ...techniques that account for changes in risk over time.
We included n=158,139 patients (n=5,007 events) receiving a first index diagnosis of a non-organic and non-psychotic mental disorder within Electronic Health Records from the SLaM NHS Foundation Trust between 01/01/2008 and 10/08/2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to TRIPOD statement. The dynamic model included 24 predictors extracted at nine landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): three demographic, one clinical, and 20 Natural Language Processing (NLP) based symptom and substance use predictors. Performance was compared to a static Cox regression model with all predictors assessed at baseline only, indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation.
The dynamic model improves discrimination performance compared to the static model at baseline (dynamic: C-index=0.9; static: C-index=0.87) to the final landmark point (dynamic: C-index=0.79; static: C-index=0.76). The dynamic model was also significantly better calibrated (calibration slope=0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher in the dynamic compared to the static model at later landmark points (≥24 months).
These findings suggest that dynamic prediction models can improve detection of individuals at risk for psychosis in secondary mental health care.
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•UiO-66 adsorbent was successfully synthesized for CO2 removal from biogas.•BioCH4 purity of 98.2 % and recovery of 94.68 % were recorded.•Dynamic model was built via Aspen Adsorption ...and validated by breakthrough curves.•Effect of temperature, CO2 content, and size ratio were studied by Aspen model.•Response surface methodology was used to optimize the studied parameters.
Biogas upgrading (CO2 removal) is an important process to produce biomethane that meets the fuel quality standards or pipelines injection specifications. Assessment of microporous UiO-66 as a potential adsorbent for CO2 removal from biogas was performed to gauge the viability of the media for methane purification and CO2 removal in pressure swing adsorption under non-isothermal conditions. UiO-66 was synthesized and characterized using various analysis tools such as SEM, BET, FTIR, XRD, TGA, particle size distribution, and TPD. A dynamic model for the proposed PSA system was built to study the effects of some parameters i.e., outside temperature, length/diameter ratio, and CO2 feed concentration on the purity and recovery of the product gases. The developed model was validated by comparing the simulation and experimental data from CH4 and CO2 breakthrough curves under similar operating conditions and adsorption bed geometry. A central composite design method was employed to optimize and examine the interaction effects of the studied parameters on the PSA system. The results confirmed the high separation performance of the PSA system using UiO-66 adsorbent with bioCH4 purity of 98.2 % and recovery of 94.68 %. The RSM model showed that the studied parameters possessed significant effects on PSA performance. The optimized parameters for achieving maximum purity and recovery were operating temperature of 290 K, length/diameter ratio of 5.6 and CO2 concentration of 39 %. Under those optimized conditions, the PSA cycle over UiO-66 recorded a purity of 99.99 %, recovery of 99.99 %, and system productivity of 8.57 mol/kg.hr.
To be able to grow crops, we have interfered with Earth's reserves of one of top three essential elements, phosphorus (P), as to which we face a problem related to its high consumption compared to ...available resources. This forces us to follow the alternative of closing the phosphorus loop from a circular economy perspective. However, there is a lack of research on regional and global social sustainability in this area, as emphasized in the United Nations' Agenda 2030 goals for sustainable development. In this paper, we address social challenges involved in global phosphorus supply chain, such as eradicating poverty, child labor and malnutrition; promoting gender equality; providing decent work and economic growth; maintaining sustainable water use; and achieving food security. Our research is driven by the question of whether the circular economy aims to direct phosphorus management towards tackling social issues associated with its supply chain. We use system dynamics modelling by combining the concept of material flow analysis and social life cycle assessment. Detailed analysis at regional and global levels indicates a paradoxical social impact of phosphorus circular model. This reflects the multiple stakeholders involved, and the regional interactions with phosphorus circular economy transitions. Improvements can be demonstrated in reducing poverty and providing safer work environment in many regions, e.g., Western Asia (93%), New Zealand, Central Asia, and Europe (44–61%), while achieving employment targets is limited in Northern and Eastern Europe. Circular model fails to promote gender equality, it also exacerbates exploitative child work problem for the Caribbean and most Africa. It provides sufficient nutrition to North America, Australia/New Zealand, and Northern Europe. It achieves water use targets in several regions with 53% savings worldwide. Finally, circular model contributes to P efficiency (average balance of 1.21 kgP/ha) and strengthens P security within most regions with an average of 64%.
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•Building model of phosphorus flow to assess social sustainable development goals•Assessment of effect of linear and circular flows on P supply chain by 2050•Estimation of social impact of P circularity at regional and global scale by 2050•Identification of paradoxical impact of phosphorus flows on social sustainability
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