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Thermal performance of photovoltaic systems integrated in buildings

Because of the central position of the chemical industries along the value chain, process design has a pivotal role, involving many decision makers and multiple levels of decisions. To tackle the environmental concern at source, this article presents a methodological framework for process ecodesign, coupling flowsheeting simulators both for production and energy processes with a Life Cycle Assessment module that generalizes and automates the evaluation of environmental impacts. The life cycle inventory is carried out through the combined use of mass and energy balances resulting both from the global simulation of the process and its associated energy production requirement and from the use of inventory database (i.e., Ecoinvent v3) embedded in the Life Cycle Assessment software tool used (SimaPro). Different process alternatives can thus be evaluated in a systematic way and the energy‐related emissions for any given process that match exactly the real situation can be computed without introducing a bias in the estimation. Through comparisons between a case base and process alternatives, a systematic decision can be made in terms of whether a solution is moving the process towards a more sustainable operation. The effectiveness of the proposed framework is first illustrated through the case study of benzene production and second, by a biodiesel production process from waste vegetable oils which is one of the foremost alternative fuels to those refined from petroleum products. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 777–796, 2018

Abstract. The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0–54.2 %), crop yield, as well as of the daily carbon and energy fluxes with NRMSE of ~9.0–20.1 and ~9.4–22.3 % for NEE, and sensible and latent heat fluxes, respectively. The model data mistfit for energy fluxes are within uncertainties of the measurements, which themselves show an incomplete energy balance closure within the range 80.6–86.3 %. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of management events. In addition, ORCHIDEE-CROP (v0) is shown to have the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, sensible heat fluxes and latent heat fluxes, across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and of management, is expected to improve its predictive ability to simulate croplands in an Earth System Model.

Abstract The focus on the green manufacturing production is becoming more and more important in France. Le Havre is a city located in the north-western France, which concentrates more than a third of French refining capacity. However, simultaneously, the pollution caused by industries needs to be controlled. Furthermore, Le Havre would like to be the first city that does really ecological industrial park in France. Industrial symbiosis is a key concept of industrial ecology, which studies the physical flows of materials and energy in the local industrial systems. There are few studies that support the assumption of economic and environmental benefits of industrial symbiosis. So, in order to have a WIN-TO-WIN situation, we proposed a mathematical optimization control model. The model will help the manufacturing industries to reduce related costs, and the negative environmental impact would be decreased simultaneously. Best to our knowledge, currently, it does not exist any other general mathematical optimization model for constructing and optimizing an eco-industrial park.

Understanding the long-term behavior of rainfall and potential evapotranspiration (PET) over watersheds is crucial for the monitoring of hydrometeorological processes and climate change at the regional scale. The São Francisco watershed (SFW) in Brazil is an important hydrological system that transports water from humid regions throughout the Brazilian semiarid region. However, long-term, gapless meteorological data with good spatial coverage in the region are not available. Thus, gridded datasets, such as the Climate Research Unit TimeSeries (CRU TS), can be used as alternative sources of information, if carefully validated beforehand. The objective of this study was to assess CRU TS (v4.02) rainfall and PET data over the SFW, and to evaluate their long-term (1942–2016) climatological aspects. Point-based measurements retrieved from rain gauges and meteorological stations of national agencies were used for validation. Overall, rainfall and PET gridded data correlated well with point-based observations (r = 0.87 and r = 0.89), with a poorer performance in the lower (semiarid) portion of the SFW (r ranging from 0.50 to 0.70 in individual stations). Increasing PET trends throughout the entire SFW and decreasing rainfall trends in areas surrounding the semiarid SFW were detected in both gridded (smoother slopes) and observational (steeper slopes) datasets. This study provides users with prior information on the accuracy of long-term CRU TS rainfall and PET estimates over the SFW.