Messias Borges Silva

Bio: Messias Borges Silva is an academic researcher from University of São Paulo. The author has contributed to research in topics: Taguchi methods & Photobioreactor. The author has an hindex of 17, co-authored 145 publications receiving 1388 citations. Previous affiliations of Messias Borges Silva include Federal University of Pernambuco & Universidade de Taubaté.

The realm of cellulases in biorefinery development

Abstract: Geopolitical concerns (unstable supply of gasoline, environmental pollution, and regular price hikes), economic, and employment concerns have been prompting researchers, entrepreneurs, and policy makers to focus on harnessing the potential of lignocellulosic feedstock for fuel ethanol production and its commercialization. The carbohydrate skeleton of plant cell walls needs to be depolymerised into simpler sugars for their application in fermentation reactions as a chief carbon source of suitable ethnologic strains for ethanol production. The role of cellulolytic enzymes in the degradation of structural carbohydrates of the plant cell wall into ready-to-fermentable sugar stream is inevitable. Cellulase synergistically acts upon plant cell wall polysaccharides to release glucose into the liquid media. Cellulase predominantly dominates all the plant cell wall degrading enzymes due to their vast and diverse range of applications. Apart from the major applications of cellulases such as in detergent formulations, textile desizing, and development of monogastric feed for ruminants, their role in biorefinery is truly remarkable. This is a major area where new research tools based upon fermentation based formulations, biochemistry, and system biology to expedite the structure-function relationships of cellulases including cellulosomes and new designer enzymatic cocktails are required. In the last two decades, a considerable amount of research work has been performed on cellulases and their application in biomass saccharification. However, there are still technical and economic impediments to the development of an inexpensive commercial cellulase production process. Advancements in biotechnology such as screening of microorganisms, manipulation of novel cellulase encoding traits, site-specific mutagenesis, and modifications to the fermentation process could enhance the production of cellulases. Commercially, cheaper sources of carbohydrates and modified fermentation conditions could lead to more cost-effective production of cellulases with the goal to reduce the cost of ethanol production from lignocellulosics. Implementation of integrated steps like cellulase production and cellulase mediated saccharification of biomass in conjunction with the fermentation of released sugars in ethanol in a single step so called consolidated bio-processing (CBP) is very important to reduce the cost of bioethanol. This paper aims to explore and review the important findings in cellulase biotechnology and the forward path for new cutting edge opportunities in the success of biorefineries.

Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays

Abstract: This work presents a study on the applicability of radial base function (RBF) neural networks for prediction of Roughness Average (R"a) in the turning process of SAE 52100 hardened steel, with the use of Taguchi's orthogonal arrays as a tool to design parameters of the network. Experiments were conducted with training sets of different sizes to make possible to compare the performance of the best network obtained from each experiment. The following design factors were considered: (i) number of radial units, (ii) algorithm for selection of radial centers and (iii) algorithm for selection of the spread factor of the radial function. Artificial neural networks (ANN) models obtained proved capable to predict surface roughness in accurate, precise and affordable way. Results pointed significant factors for network design have significant influence on network performance for the task proposed. The work concludes that the design of experiments (DOE) methodology constitutes a better approach to the design of RBF networks for roughness prediction than the most common trial and error approach.

Artificial neural networks for machining processes surface roughness modeling

Abstract: In recent years, several papers on machining processes have focused on the use of artificial neural networks for modeling surface roughness. Even in such a specific niche of engineering literature, the papers differ considerably in terms of how they define network architectures and validate results, as well as in their training algorithms, error measures, and the like. Furthermore, a perusal of the individual papers leaves a researcher without a clear, sweeping view of what the field’s cutting edge is. Hence, this work reviews a number of these papers, providing a summary and analysis of the findings. Based on recommendations made by scholars of neurocomputing and statistics, the review includes a set of comparison criteria as well as assesses how the research findings were validated. This work also identifies trends in the literature and highlights their main differences. Ultimately, this work points to underexplored issues for future research and shows ways to improve how the results are validated.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Conceptualising sustainability assessment

Abstract: Sustainability assessment is being increasingly viewed as an important tool to aid in the shift towards sustainability. However, this is a new and evolving concept and there remain very few examples of effective sustainability assessment processes implemented anywhere in the world. Sustainability assessment is often described as a process by which the implications of an initiative on sustainability are evaluated, where the initiative can be a proposed or existing policy, plan, programme, project, piece of legislation, or a current practice or activity. However, this generic definition covers a broad range of different processes, many of which have been described in the literature as 'sustainability assessment'. This article seeks to provide some clarification by reflecting on the different approaches described in the literature as being forms of sustainability assessment, and evaluating them in terms of their potential contributions to sustainability. Many of these are actually examples of 'integrated assessment', derived from environmental impact assessment (EIA) and strategic environmental assessment (SEA), but which have been extended to incorporate social and economic considerations as well as environmental ones, reflecting a 'triple bottom line' (TBL) approach to sustainability. These integrated assessment processes typically either seek to minimise 'unsustainability', or to achieve TBL objectives. Both aims may, or may not, result in sustainable practice. We present an alternative conception of sustainability assessment, with the more ambitious aim of seeking to determine whether or not an initiative is actually sustainable. We term such processes 'assessment for sustainability'. 'Assessment for sustainability' firstly requires that the concept of sustainability be well-defined. The article compares TBL approaches and principles-based approaches to developing such sustainability criteria, concluding that the latter are more appropriate, since they avoid many of the inherent limitations of the triple-bottom-line as a conception of sustainability.

Green Carbon Science: Scientific Basis for Integrating Carbon Resource Processing, Utilization, and Recycling

Abstract: The general principles of green chemistry guide the design of environmentally benign products and processes. It is known that the efficient utilization of carbon resources and carbon recycling are of great importance for the sustainable development of our society, and there are many related challenging scientific issues to be solved. Herein we attempt a holistic view on carbon resource processing, utilization, and recycling, and we call this “green carbon science”. We will discuss the definition and the four key principles of green carbon science and highlight some related fields including petroleum refining, the production of liquid fuels and chemicals from coal and methane, and transformations of CO2 and biomass into fuels and chemicals.