Life cycle of hydroprocessing catalysts and total catalyst management

Hydroprocessing catalysts based on Ni, Co, Mo and W are used in various refinery processing applications where several deactivation mechanisms become of importance (coke formation, active phase sintering, metals deposition, poisoning) in the catalyst's life cycle. The life cycle of commercial hydroprocessing catalysts is very complex and includes the catalyst production, sulfidation, use, oxidative regeneration followed by re-sulfidation and reuse or, if reuse is not possible, recycling or disposal. To understand the changes in catalyst properties taking place during a life cycle, the catalyst quality in the different stages can be best monitored by using advanced analytical techniques. The catalyst's life cycle is further complicated by numerous technical, environmental and organizational issues involved. In principle, different companies can be involved in each of the life cycle steps. Leading catalyst manufacturers, together with specialized firms, offer refineries a total catalyst management concept, starting with the purchase of the fresh catalyst and ending with its final recycling or disposal. Total catalyst management includes a broad range of services, ensuring optimal timing during the change-out process, reliable, smooth and safe operations, minimal downtime and maximum catalyst and unit performance.     

Techno-economic study of different alternatives for biodiesel production

Biodiesel has become an attractive diesel fuel substitute due to its environmental benefits since it can be made from renewable resource. However, the high costs surrounding biodiesel production remains the main problem in making it competitive in the fuel market either as a blend or as a neat fuel. More than 80% of the production cost is associated with the feedstock itself and consequently, efforts are focused on developing technologies capable of using lower-cost feedstocks, such as recycled cooking oils and wastes from animal or vegetable oil processing operations.     

The influence of raw material characteristics on the industrial pelletizing process and pellet quality

Industrial pelletizing of sawdust was carried out as a designed experiment in the factors: sawdust moisture content, fractions of fresh pine, stored pine and spruce. The process parameters and response variables were energy consumption, pellet flow rate, pellet bulk density, durability and moisture content. The final data consisted of twelve industrial scale runs. Because of the many response variables, data evaluation was by principal component analysis of a 12 × 9 data matrix. The two principal component model showed a clustering of samples, with a good reproducibility of the center points. It also showed a positive correlation of energy consumption, bulk density and durability all negatively correlated to flow rate and moisture content. The stored pine was more related to high durability and bulk density. The role of the spruce fraction was unclear. The design matrix, augmented with the process parameters was a 12 × 6 matrix. Partial least squares regression showed excellent results for pellet moisture content and bulk density. The model for durability was promising. A 12 × 21 data matrix of fatty- and resin acid concentrations measured by GC–MS showed the differences between fresh and stored pine very clearly. The influence of the spruce fraction was less clear. However, the influence of the fatty- and resin acids on the pelletizing process could not be confirmed, indicating that other differences between fresh and stored pine sawdust have to be investigated. This work shows that it is possible to design the pelletizing process for moderate energy consumption and high pellet quality.     

Logistic Regression Analysis of Customer Satisfaction Data

Variation exists in all processes. Significant work has been done to identify and remove sources of variation in manufacturing processes resulting in large returns for companies. However, business process optimization is an area that has a large potential return for a company. Business processes can be difficult to optimize due to the nature of the output variables associated with them. Business processes tend to have output variables that are binary, nominal or ordinal. Examples of these types of output include whether a particular event occurred, a customer's color preference for a new product and survey questions that assess the extent of the survey respondent's agreement with a particular statement. Output variables that are binary, nominal or ordinal cannot be modeled using ordinary least‐squares regression. Logistic regression is a method used to model data where the output is binary, nominal or ordinal. This article provides a review of logistic regression and demonstrates its use in modeling data from a business process involving customer feedback. Copyright © 2006 John Wiley & Sons, Ltd.     

CO2 emissions in Greece for 1990–2002: A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques

This paper deals with the decomposition analysis of energy-related CO₂ emissions in Greece from 1990 to 2002. The Arithmetic Mean Divisia Index (AMDI) and the Logarithmic Mean Divisia Index (LMDI) techniques are applied and changes in CO₂ emissions are decomposed into four factors: income effect, energy intensity effect, fuel share effect and population effect. The period-wise and time series analyses show that the biggest contributor to the rise in CO₂ emissions in Greece is the income effect; on the contrary, the energy intensity effect is mainly responsible for the decrease in CO₂ emissions. A comparison of the results of the two techniques gave an insight in the intricacies of energy decomposition. Finally, conclusions and future areas of research are presented.     

Automating application software generation

Many approaches have been proposed to enhance software productivity and reliability. These approaches typically fall into three categories: the engineering approach, the formal approach and the knowledge-based approach. But the optimal gain in software productivity cannot be obtained if one relies on only one of these approaches. This paper describes the work in knowledge-based software engineering conducted by the authors for the past 10 years. The final goal of the research is to develop a paradigm for software engineering which integrates the three approaches mentioned above. A knowledge-based tool which can support the whole process of software development is provided in this paper.     

An analysis of shielding against DC magnetic fields generated by electric railways. Evaluation of effective permeability of fine structure

This paper describes an effective analysis of magnetic shielding based on homogenization. The analyses become time‐consuming if the problems include the magnetic substances having fine structure. The homogenization of the structure makes it possible to analyze effectively the magnetic fields. The authors introduce a method to estimate the effective permeability of the homogenized substance. This method can be applied to any periodic structure made of magnetic substance. The magnetic shielding effects by the structures against direct‐current (DC) fields generated by electric railways are analyzed by using the present method. As a result, it is found that the overhead way and the protective fence near the railway work as a magnetic shield, whose effects can be improved by appropriate arrangement of those constructions. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 160(4): 7–15, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20310