Fluid catalytic cracking optimisation using factorial design and genetic algorithm techniques

Statistical techniques coupled with genetic algorithm (GA) were used to identify optimal values of key operational variables in fluid catalytic cracking (FCC) process. A Kellog Orthoflow F fluid catalytic cracking process model was considered. It is known as a highly nonlinear process with a large number of variables with strong interactions among them. A reduced process model was obtained through factorial design technique to be used as a process function in the optimisation work giving as result the operational conditions that maximise conversion without infringing operational restrictions with savings in computational burden and time. An increase of 8.71% in process conversion was achieved applying GA as optimisation technique. © 2012 Canadian Society for Chemical Engineering     

A new engine indicating measurement procedure for combustion heat release analysis

This paper presents a new arrangement for the indicating measurement system utilized in the study of internal combustion engines. In this experimental configuration, the current polarized by a piezoelectric pressure transducer is converted into an analog signal proportional to the cylinder pressure variation rate, which constitutes the primary information for combustion heat release analysis. The proposed technique reduces the uncertainty in the pressure derivative data, providing more accurate heat release results than those obtained with the traditional transducer signal conditioning procedure, which requires the numerical derivation of in-cylinder pressure data supplied by a charge amplifier.     

A modular neural network for super-resolution of human faces

This paper presents the original and versatile architecture of a modular neural network and its application to super-resolution. Each module is a small multilayer perceptron, trained with the Levenberg-Marquardt method, and is used as a generic building block. By connecting the modules together to establish a composition of their individual mappings, we elaborate a lattice of modules that implements full connectivity between the pixels of the low-resolution input image and those of the higher-resolution output image. After the network is trained with patterns made up of low and high-resolution images of objects or scenes of the same kind, it will be able to enhance dramatically the resolution of a similar object’s representation. The modular nature of the architecture allows the training phase to be readily parallelized on a network of PCs. Finally, it is shown that the network performs global-scale reconstruction of human faces from very low resolution input images.     

Structural elements of coordination mechanisms in collaborative planning processes and their assessment through maturity models: Application to a ceramic tile company

Maturity is defined as a measure to evaluate the capabilities of an organization in regards to a certain discipline. The Collaborative Planning Process is a very complex process and Coordination mechanisms are especially relevant in this field to align the plans of the supply chain members. The objective of this paper is to develop a maturity model and a methodology to perform assessment for the Structural Elements of Coordination Mechanisms in the Collaborative Planning Process. Structural elements are specified in order to characterize coordination mechanisms in a collaborative planning context and they have been defined as key areas to be assessed by the maturity model. The identified structural elements are: number of decision-makers, collaboration level, interdependence relationships nature, interdependence relationships type, number of coordination mechanisms, information exchanged, information processing, decision sequence characteristics and stopping criteria. Structural elements are assessed using the scheme of five levels: Initial, Repeatable, Defined, Managed and Optimized. This proposal has been applied to a ceramic tile company and the results are also reported.     

Characterization, modeling and assessment of Roll-N-Cage isolator using the cable-stayed bridge benchmark

This paper presents the results of an extensive series of simulation tests to identify the mechanical characteristics of an innovative isolation device known as the Roll-N-Cage (RNC) isolator. The seismic performance of an RNC passive control scheme is subsequently investigated on a model of the cable-stayed bridge benchmark. Starting from different configurations studied in the laboratory for a 1/10 reduced-scale prototype, the RNC isolator stiffness and damping properties are investigated in terms of cyclic tests with different parameters. Tests at the ultimate level state consisting of monotonic shear and axial loading have been also carried out as a part of the qualification process. The goal of this study is twofold: first, to examine the main integrated mechanisms of the RNC isolator through sophisticated 3D finite element simulation models using a multi-purpose finite element code. The main result of this step is to attempt modeling the force–displacement relationship using the standard Bouc–Wen model of smooth hysteresis. The second aim of this study is the numerical assessment of the device efficiency through its implementation into a bridge model considering several ground motions as external excitations. Based on these extensive studies, it was found that the RNC isolator is promising as a reliable isotropic horizontal isolation device for bridge structures.     

Silicon-doped hydroxyapatite prepared by a thermal technique for hard tissue engineering applications

Hydroxyapatite (HA, Ca₅(PO₄)₃OH) has been extensively used for bone implantation due to its similarity to the mineral component of bone, which makes it strongly osteoconductive. However, HA has low resorbability, and it is difficult to replace by a newly regenerated bone. Si doping can enhance the resorbability of HA by modifying its crystal structure. Here, we developed a simple thermal technique for preparing Si-doped HA from silica (SiO₂) and HA precursors, both of which are inexpensive and commercially available. This method included the physical binding of SiO₂ and HA particles, followed by pressing and sintering the mixture at an elevated temperature, which enhanced the atomic diffusion of Si into HA unit cells. We also evaluated the simulated body fluid (SBF) activity of the Si-doped HA prepared by this technique and showed that it significantly had higher resorbability and mineralizing potential compared to the pure HA. Our experimental design including, the individual precipitation and resorption assays enabled us to explain the mechanism behind the improved activity of Si-doped HA in SBF. This was attributed to the formation of new phases, such as β-tricalcium phosphate (β-TCP) and calcium silicate (Ca₂SiO₄) with higher solubility than HA on the SiO₂-contating HA during the sintering stage. This can provide some guidelines for designing new calcium phosphate-based materials for hard tissue engineering applications.