Formulation,calibration, and validation of hybrid and coupled models for the design of upflow anaerobic filters in multiple separated stages for organic matter removal from sanitary landfill leachates

In this paper, the formulation, calibration, and validation of hybrid and coupled models for the design of upflow anaerobic filters in multiple separated stages were developed for organic matter removal from sanitary landfill leachates. Three novelties were presented, the type of reactor, design models, and kinetic coefficients. The upflow anaerobic filters were separated into two and three stages identified as UAF-2SS (DI-FAFS, in Spanish) and UAF-3SS (TRI-FAFS, in Spanish). The formulation, calibration, and validation of mathematical structures of hybrid models and five coupled models are proposed for each reactor. The hybrid models are based on the law of mass conservation, with the organic matter transformation component within the UAF-2SS and UAF-3SS reactors, being estimated from empirical equations that have been tested in aerobic culture reactors, adapted to the experimental factors, including among these, those under a non-stationary—advective conditions based on Velz's Law, Phelps's Law, and Monod's equation. The coupled models combine the components of the molecular transport by biosorption and molecular diffusion processes, with adaptations of the Stack's equation and Fick's Law, as well as transformation of organic substrates by biomass, whose kinetic coefficients contribute to explain the fraction, in which, the processes of mobility and biochemical transformation of the organic matter are occurring in the biomass within the bioreactors.     

Theoretical evaluation of lower explosion limit of hybrid mixtures

Lower explosion limits of hybrid fuel mixtures are usually determined through time consuming and expensive experiments. Although, mathematical expressions like Le-Chatelier's Law and Bartknecht curve have been used by many researchers to predict the LEL of hybrid mixtures, significant deviations remain unexplained. This research work, presents a more sophisticated and general approach for the determination of LEL of hybrid mixtures.Assuming that the combustion kinetics of pure species are independent and unchanged by the presence of other combustible species, complete conversion of the reactants and no heat losses, a simple mathematical model has been derived from the enthalpy balance of the whole system. For the experimental validation of the modelled values, modified version of 20L sphere has been employed, following the European standard (EN 14034-3: 2011) as experimental protocol. Hybrid mixtures of three dusts with two gases were selected for the scope of this publication. By analyzing the modelled as well as the experimental values, it can be concluded that the LEL values of the individual components in the hybrid mixture set the upper and lower limit for the LEL of the hybrid mixture provided the total amount of fuel in the system is considered as the concentration of the hybrid mixture. Moreover, the amount of dust or gas required to render the hybrid mixture flammable mainly depends on the energy contribution upon combustion of the individual species to raise the temperature of the whole system from ambient to the flame temperature.Le-Chatelier's Law and Bartknecht curve are empirical relations, which might hold true for a first-order approximation of LEL of hybrid mixtures, but do not represent the most conservative values of LEL reported in literature. This implies that there is a non-zero probability of occurrence of an explosible mixture in the non-explosible concentrations ranges defined by these relations. Considering these arguments, the authors suggest to employ the model presented in this paper – which presents reasonably conservative values of LEL of hybrid mixtures – for theoretical calculation of LEL of hybrid mixtures, when no precise experimental data is available.     

Linkages between measures of biodiversity and community resilience in Pacific Island agroforests

Designing agroecosystems that are compatible with the conservation of biodiversity is a top conservation priority. However, the social variables that drive native biodiversity conservation in these systems are poorly understood. We devised a new approach to identify social–ecological linkages that affect conservation outcomes in agroecosystems and in social‐ecological systems more broadly. We focused on coastal agroforests in Fiji, which, like agroforests across other small Pacific Islands, are critical to food security, contain much of the country's remaining lowland forests, and have rapidly declining levels of native biodiversity. We tested the relationships among social variables and native tree species richness in agroforests with structural equation models. The models were built with data from ecological and social surveys in 100 agroforests and associated households. The agroforests hosted 95 native tree species of which almost one‐third were endemic. Fifty‐eight percent of farms had at least one species considered threatened at the national or international level. The best‐fit structural equation model (R² = 47.8%) showed that social variables important for community resilience—local ecological knowledge, social network connectivity, and livelihood diversity—had direct and indirect positive effects on native tree species richness. Cash‐crop intensification, a driver of biodiversity loss elsewhere, did not negatively affect native tree richness within parcels. Joining efforts to build community resilience, specifically by increasing livelihood diversity, local ecological knowledge, and social network connectivity, may help conservation agencies conserve the rapidly declining biodiversity in the region.     

Power management for hybrid distributed generation systems

The design of a new power management tool able to manage the power flow from different renewable energy sources is proposed in this paper. PV and wind are the primary power sources for the system, and a fuel cell with electrolyser and batteries are used as reserve. The designed controller purpose is to manage power flows among the different energy systems and to assure a continuous supply of load. Modelling and simulation of the various energy sources of distributed generation (DG) systems including wind turbine (WT), fuel cell-electrolyser (FC), photovoltaic (PV) and battery are developed. The coordination controller is designed based on the criteria of providing the load demand and the excess power is used either to produce hydrogen through electrolyser for the FC or to store it in battery. Simulation is carried out in MATLAB/Simulink environment and the results show satisfactory performance of the coordination scheme to satisfy the load requirements.     

Numerical analysis of toxic cloud generation and dispersion: A case study of the ethylene oxide spill

The present study examined the accidental spill of ethylene oxide, and a sensitivity analysis of the corresponding consequences was conducted using computational fluid dynamics (CFD). A validation of the gas dispersion CFD model against the experimental data sets included in the model evaluation protocol (MEP) was performed. The effect of the variability of the wind velocity on the extension of the hazardous areas and pool evaporation characteristics was evaluated. Additionally, the mitigation effects of the dike walls surrounding a spill were discussed. CFD simulation results have shown that the mitigation effect of dike walls is determined by their influence on both gas dispersion and pool evaporation and depends strongly on wind velocity in terms of toxic impact distances.     

Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis

A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM), was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues >1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values, with R² = 0.80. For SEM, Chl a and eight variables were used: NH₄-N (ammonia-nitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence of water chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu.     

Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

Modelling coral reef ecosystems with limited observational data

Ecosystem models represent potentially powerful tools for coral reef ecosystem managers. They can provide insight into ecosystem dynamics not achievable through alternative means allowing coral reef managers to assess the potential outcome of any given management decision. One of the main limitations in the applicability of ecosystem models is that they often require detailed empirical data and this can restrict their applicability to ecosystems that are either currently well studied or have the resources available to collect the required data. This study describes the development of a coral reef ecosystem model that can be calibrated to an ecosystem with limited empirical data. Based on the assumption that coral reef ecological structure is generic across all tropical coral reefs and that the magnitude of the interactions between ecological components is reef specific, the dynamics of the ecosystem can be replicated based on limited empirical data. The model successfully replicated the dynamics of three individual reef systems including an inshore and oceanic reef within the Great Barrier Reef and a Caribbean reef system. It highlighted the importance of understanding the specific dynamics of a given reef and that a positive management intervention in one system may result in a negative outcome for another. The model was also used to assess the importance of various interactions within coral reef ecosystems. It identified the interactions between hard corals and other non-algal benthic components as being an important (but currently understudied) facet of coral reef ecology. The development of this modelling approach provides access to ecosystem modelling tools for coral reef managers previously excluded due to a lack of resources or technical expertise.     

Scenarios of global bioenergy production: The trade-offs between agricultural expansion, intensification and trade

Increased future demands for food, fibre and fuels from biomass can only be met if the available land and water resources on a global scale are used and managed as efficiently as possible. The main routes for making the global agricultural system more productive are through intensification and technological change on currently used agricultural land, land expansion into currently non-agricultural areas, and international trade in agricultural commodities and processed goods. In order to analyse the trade-offs and synergies between these options, we present a global bio-economic modelling approach with a special focus on spatially explicit land and water constraints as well as technological change in agricultural production. For a global bioenergy demand scenario reaching 100 ExaJoule (EJ) until 2055 we derive a required rate of productivity increase on agricultural land between 1.2 and 1.4 percent per year under different land allocation options. A very high pressure for yield increase occurs in Sub-Saharan Africa and the Middle East, even without additional bioenergy demand. Moreover, we analyse the implicit values (shadow prices) of limited water resources. The shadow prices for bioenergy are provided as a metric for assessing the trade-offs between different land allocation options and as a link between the agricultural and energy sector.