Energy-Process Integration of the Gas-Cooled/Water-Cooled Fixed-Bed Reactor Network for Methanol Synthesis

The paper deals with the techno-economical assessment of the gas-cooled/water-cooled fixed-bed reactor network for methanol synthesis. The study is the extension of the first-principles model for the watercooled reactor already proposed in our prior work (Manenti et al., 2013). Here, the optimization is extended to the steam generation and the reactor length ratio. As a result, basing on the integrated optimization of energy and process yield, we propose to significantly revise the common design. The traditional water/gas cooling reactor length ratio could be significantly reduced with consequent simultaneous increase in methanol production and steam generation as well, however preserving safety and operational ranges. The economic benefit deriving from the proposed design for a medium-scale process is estimated in more than 1.7 M€/y

Is there a need for a forest restoration certification scheme?

We propose the development of a certification scheme for forest ecosystem restoration that aims for the adoption of protocols and guidelines to ensure the sustained ecological and social value of restored ecosystems. Despite an accumulation of experience on ecosystem restoration over the past decades, it is still common to measure the success of restoration mainly in terms of number of seedlings planted or their survival in the short term. A strong focus on planting targets may divert attention from the actual objectives: establish self-sustaining forested ecosystems that provide livelihood or other ecosystem service benefits to local people. Two important determinants of short and long term success, which often do not receive sufficient attention, are matching the right seed source to the planting site conditions and ensuring that restored populations of trees have sufficient genetic variability to be self-sustaining. Because of the enormous scale of land degradation and the funds being pledged to tackle it, standardized measures of success are of increasing importance. Restoration success needs to be evaluated in a holistic way by restoration practitioners, government institutions, civil society organizations, private sector and, importantly, funding agencies. Much is known about how to restore ecosystems in different regions and under different conditions, however currently there is no consensus on what success looks like or what the minimum criteria should be for monitoring and documenting success. Success can be achieved by following well defined practices and protocols (eg by ensuring high diversity both at species and genes levels, number of mother trees for the collection of reproductive material, provenance, etc) during the various phases of the restoration process. We make a case for the development of a certification system to support long term value of restored populations for global application

Temporal variations of zooplankton biomass in the Ligurian Sea inferred from long time series of ADCP data

Abstract. Three years of 300 kHz acoustic doppler current profiler data collected in the central Ligurian Sea are analysed to investigate the variability of the zooplankton biomass and the diel vertical migration in the upper thermocline. After a pre-processing phase aimed at avoiding the slant range attenuation, hourly volume backscattering strength time series are obtained. Despite the lack of concurrent net samples collection, different migration patterns are identified and their temporal variability examined by means of time–frequency analysis. The effect of changes in the environmental condition is also investigated. The highest zooplankton biomasses are observed in April–May just after the peak of surface primary production in March–April. The main migration pattern found here points to a "nocturnal" migration, with zooplankton organisms occurring deeper in the water column during the day and shallower at night. Also, twilight migration is highlighted during this study. The largest migrations are recorded in November–December, corresponding to lowest backscattering strength values and they are likely attributable to larger and more active organisms (i.e. euphausiids and mesopelagic fish). The results suggest further applications of the available historical acoustic doppler current profiler time series

Introducing temporal correlation in rainfall and wind prediction from underwater noise

While in the past the prediction of wind and rainfall from underwater noise was performed using empirical equations fed with very few spectral bins and fitted to the data, it has recently been shown that regression performed using supervised machine learning techniques can benefit from the simultaneous use of all spectral bins, at the cost of increased complexity. However, both empirical equations and machine learning regressors perform the prediction using only the acoustic information collected at the time when one wants to know the wind speed or the rainfall intensity. At most, averages are made between spectra measured at subsequent times (spectral compounding) or between predictions obtained at subsequent times (prediction compounding). In this article, it is proposed to exploit the temporal correlation inherent in the phenomena being predicted, as has already been done in methods that forecast wind and rainfall from their values (and sometimes those of other meteorological quantities) in the recent past. A special architecture of recurrent neural networks, the long shortterm memory, is used along with a data set composed of about 16 months of underwater noise measurements (acquired every 10 min, simultaneously with wind and rain measurements above the sea surface) to demonstrate that the introduction of temporal correlation brings significant advantages, improving the accuracy and reducing the problems met in the widely adopted memoryless prediction performed by random forest regression. Working with samples acquired at 10-min intervals, the best performance is obtained by including three noise spectra for wind prediction and six spectra for rainfall prediction

compass 3 0

COMPASS (COrrectness, Modeling and Performance of AeroSpace Systems) is an international research effort aiming to ensure system-level correctness, safety, dependability and performability of on-board computer-based aerospace systems. In this paper we present COMPASS 3.0, which brings together the results of various development projects since the original inception of COMPASS. Improvements have been made both to the frontend, supporting an updated modeling language and user interface, as well as to the backend, by adding new functionalities and improving the existing ones. New features include Timed Failure Propagation Graphs, contract-based analysis, hierarchical fault tree generation, probabilistic analysis of non-deterministic models and statistical model checking