Maximization of propylene in an industrial FCC unit

YesThe FCC riser cracks gas oil into useful fuels such as gasoline, diesel and some lighter products such as ethylene and propylene, which are major building blocks for the polyethylene and polypropylene production. The production objective of the riser is usually the maximization of gasoline and diesel, but it can also be to maximize propylene. The optimization and parameter estimation of a six-lumped catalytic cracking reaction of gas oil in FCC is carried out to maximize the yield of propylene using an optimisation framework developed in gPROMS software 5.0 by optimizing mass flow rates and temperatures of catalyst and gas oil. The optimal values of 290.8 kg/s mass flow rate of catalyst and 53.4 kg/s mass flow rate of gas oil were obtained as propylene yield is maximized to give 8.95 wt%. When compared with the base case simulation value of 4.59 wt% propylene yield, the maximized propylene yield is increased by 95%

Compositional and urban form effects on residential property value patterns in Greater London

The objective of this research is to determine the role of urban street layout design in the process of shaping property values. The effect of spatial accessibility on rent is a classic finding of spatial economics. Using space syntax fine-grained spatial design analysis, which indexes the spatial centrality and accessibility, the patterns of property prices are analysed for a large contiguous sample of over 60 000 residential dwellings in a North London borough, using the council tax band as a proxy variable for the property price. Few studies have examined the effect of spatial contiguity on the housing sub-market classification. The findings demonstrate that the council tax band proxy is a good indicator of residential property sale prices. In addition, a hedonic model framework shows that spatial centrality and accessibility, as indexed by the space syntax spatial design analysis, accounts for the variations in residential property values for single and multiple dwellings when controlling for the property size, relative density and building age. Multivariate analysis is used to establish the weighting of the different variables. The single most important spatial factor is the property size, followed by the ambient density, the local and global spatial accessibility and the building age. Non-residential land use location, the proximity to main arterial roads and the associated traffic and air pollution are shown to inhibit the residential property location

Mode-of-Action-Guided, Molecular Modeling-Based Toxicity Prediction: A Novel Approach for \u3ci\u3eIn Silico\u3c/i\u3e Predictive Toxicology

Computational toxicology is a sub-discipline of toxicology concerned with the development and use of computer-based models and methodology to understand and predict chemical toxicity in a biological system (e.g., cells and organisms). Quantitative structure–activity relationship (QSAR) has been the predominant approach in computational toxicology. However, classical QSAR methodology has often suffered from low prediction accuracy, largely owing to the lack or non-integration of toxicological mechanisms. To address this lingering problem, we have developed a novel in silico toxicology approach that is based on molecular modeling and guided by mode of action (MoA). Our approach is implemented through a target-specific toxicity knowledgebase (TsTKb), consisting of a pre-categorized database of chemical MoA (ChemMoA) and a series of pre-built, category-specific classification and quantification models. ChemMoA serves as the depository of chemicals with known MoAs or molecular initiating events (i.e., known target biomacromolecules) and quantitative information for measured toxicity endpoints (if available). The models allow a user to qualitatively classify an uncharacterized chemical by MoA and quantitatively predict its toxicity potency. This approach is currently under development and will evolve to incorporate physiologically based pharmacokinetic (PBPK) modeling to address absorption, distribution, metabolism and excretion (ADME) processes in a biological system. The fully developed approach is believed to significantly advance in silico -based predictive toxicology and provide a new powerful toolbox for regulators, the chemical industry and the relevant academic communities