Ca-amendement and tillage: medium term synergies for improving key soil properties of acid soils. Soil and tillage research, 134: 195-206

Ca-amendments are routinely applied to improve acid soils, whilst no-tillage (NT) has been widely recommended in soils where traditional tillage (TT) has led to losses of organic matter. However, the potential interactions between the two treatments are only partially known. Our study was conducted on an annual forage crop agrosystem with a degraded Palexerult soil located in SW Spain, in order to assess if the combination of NT plus a Ca-amendment provides additional benefits to those of their separate use. To this end we analysed the effects of four different combinations of tillage and Ca-amendment on selected key soil properties, focusing on their relationships. The experimental design was a split-plot with four replicates. The main factor was tillage (NT versus TT) and the second factor was the application or not of a Ca-amendment, consisting of a mixture of sugar foam (SF) and red gypsum (RG). Soil samples were collected from 3 soil layers down to 50 cm after four years of treatment (2009). The use of the Ca-amendment improved pH and Al-toxicity down to 25 cm and increased exchangeable Ca2+ down to 50 cm, even under NT due to the combined effect of SF and RG. Both NT and the Ca-amendment had a beneficial effect on total organic carbon (TOC), especially on particulate organic carbon (POC), in the 0–5 cm layer, with the highest contents observed when both practices were combined. Unlike NT, the Ca-amendment failed to improve soil aggregation in spite of the carbon supplied. This carbon was not protected within the stable aggregates in the medium term, making it more susceptible to mineralization. We suggest that the fraction of Al extracted by oxalate from solid phase (AlOxa-Cu-K) and the glomalin-related soil proteins (GRSPs) are involved in the accumulation of carbon within water stable aggregates, probably through the formation of non-toxic stable Al-OM compounds, including those formed with GRSPs. NT alone decreased AlK in the 0–5 cm soil layer, possibly by increasing POC, TOC and GRSPs, which were observed to play a role in reducing Al toxicity. From our findings, the combination of NT and Ca-amendment appears to be the best management practice to improve chemical and physical characteristics of acid soils degraded by tillage.

The Zebrafish Information Network (ZFIN): a resource for genetic, genomic and developmental research

The Zebrafish Information Network, ZFIN, is a WWW community resource of zebrafish genetic, genomic and developmental research information (http://zfin.org). ZFIN provides an anatomical atlas and dictionary, developmental staging criteria, research methods, pathology information and a link to the ZFIN relational database (http://zfin.org/ZFIN/). The database, built on a relational, object-oriented model, provides integrated information about mutants, genes, genetic markers, mapping panels, publications and contact information for the zebrafish research community. The database is populated with curated published data, user submitted data and large dataset uploads. A broad range of data types including text, images, graphical representations and genetic maps supports the data. ZFIN incorporates links to other genomic resources that provide sequence and ortholog data. Zebrafish nomenclature guidelines and an automated registration mechanism for new names are provided. Extensive usability testing has resulted in an easy to learn and use forms interface with complex searching capabilities.

Real time optimization in chemical process: evaluation of strategies, improvements and industrial application.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process.

Überlegungen zu Ressourceneffizienz und Recycling = Reflections on resource efficiency and recycling. Research Paper in Law 6/2003

No abstract.