An energy analysis on the production of torrefied microalgal biomass

© 2020 Institute of Physics Publishing. All rights reserved. Torrefaction is a process for upgrading raw biomass into an energy-dense fuel. In this study, an energy analysis was conducted to assess the energy consumption in the production of torrefied microalgal biomass. The functional unit of one kilogram torrefied biomass and a system boundary of cradle-to-gate was used. This includes the life cycle stages of cultivation, harvesting, drying, and torrefaction. To include the varying method for the upstream processes, four different scenarios of torrefied biomass production are considered. The result of the analysis revealed that across all four scenarios, the torrefaction stage had a minimal contribution of 1-20% as compared with other life cycle stages. However, even with very optimistic assumptions among all scenarios, the result of the study shows a large energy deficit on the system due to the high energy consumption involved in the cultivation method and even in the drying process. To minimize energy consumption during the cultivation stage, solar lighting was highly recommended. The use of a solar-assisted drying was also advisable to lessen the energy consumption for the drying stage

A computational architecture for evaluation of the fuel consumption for importation of products

© 2017 IEEE. Transportation is one of the leading sources of carbon dioxide emission of the world. Since, globally, it is a network of links between destinations, the assessment of its carbon footprint is difficult to estimate. Life Cycle Assessment is often used in assessing the environmental impact of each link, however, most studies account only for a specific configuration of the transportation network. This study proposes to generalize the supply chain network of product flow by incorporating the database of the distances between ports and the product flow, in order to quantify the fuel consumed for importing a product. The fuel consumption quantified by the developed model can aid environmental scientists to arrive in a more comprehensive impact assessment. Furthermore, the study will assist policy and decision makers in the evaluation of supply chains. A case study on the transportation of petroleum oil is used to demonstrate the capability of the developed model. The yielded result of the model was able to portray the regions of import where there is high fuel consumption

A comparative life cycle analysis of plastic and paper packaging bags in the Philippines

Various cities in the Philippines have started to prohibit the use of plastic bags and packaging materials in favor of paper products for waste disposal and management reasons. This study evaluated the soundness of these initiatives based on life-cycle analysis (LCA) framework. While a number of studies have looked at similar issues in other countries, results may not be entirely valid in the Philippines due to different variations in energy and material supply chain and waste disposal practices and system. Considering the usual products being purchased by a Filipino family and the amount, 12 liter sando bags and 14 liter paper bag capacity were used as the functional units for the research. Comparison of the impact assessment was done by looking into the cradle-to-grave processes of the two bag materials. The study covered disposal to land, air and water effluents and included the global warming, acidification, ozone depletion and human toxicity impact areas. A modified EDIP was used for the life-cycle inventory and results show that out of the four impact factors, three favored the use of plastic bags. Future studies may be done on other impact factors as well as on other bag materials. This study was commissioned by the Department of Environment and Natural Resources (DENR) to aid policy development in waste management in the country. © 2015 IEEE

Multi-objective optimal synthesis of algal biorefineries toward a sustainable circular bioeconomy

Production of biodiesel from renewable resources like microalgae biomass presents a potential for reduction of greenhouse gas emissions and fossil fuel energy consumption. The integration of processes from other industries have been implemented in microalgal biorefineries to increase economic sustainability by co-producing several high-value algal-based products. Agro-industrial processes have the potential to be incorporated into the biorefinery because it requires input material flows from other biorefinery process units to cultivate and sell crops for an additional source of revenue and increased carbon sequestration, while generating wastewater that may be used as a cultivation medium for algae or as a resource for other biorefinery processes. Circular bioeconomy, an extension of the circular economy ideology, has the goal of achieving economic and environmental sustainability through maximizing the dedicated recirculation of resource flows, and minimizing waste generation and end-of-life disposal. However, existing modelling studies have not explored this opportunity; previous studies have not considered that resource functionality runs out with repeated recirculation and reuse as it reaches its end of life. In this work, a novel multi-objective optimization model is developed to design and manage closed-loop algal biorefineries integrating agro-industrial processes that captures the effect of recirculation on resource material viability and end-of-life environmental impact. A case study is solved as proof of concept and to illustrate the design methodology, optimal solutions based on economic and environmental performance are analyzed. The results of the case study validate the initial hypothesis that there is a conflict between the economic and environmental objectives since the decision for biofuel production varied for each single objective. With the multi objective model, a balance between the two objectives was found. The results of the optimization model can be applied in the design of an algal biorefinery along with the decisions relating to production quantities incorporating a zero waste outlook. © 2020 Institute of Physics Publishing. All rights reserved

Finite element analysis on the factors affecting die crack propagation in BGA under thermo-mechanical loading

Ball grid array (BGA) is one of the most innovative semiconductor packaging technologies which is capable of high input-output capacities while addressing handling and coplanarity compared with other packages. However, the BGA package is subjected to thermo-mechanical load which makes it susceptible to quality and reliability issues such as die crack. The occurrence of die crack is difficult to monitor as it is considered as an internal package issue and can be catastrophic to the electronic device which may lead to its failure. This study aims to investigate the various factors affecting die crack propagation using finite element analysis (FEA) model under thermo-mechanical loads. The energy release rate in the silicon die was used to quantify the propagation of die crack in the BGA package. The influence of the various factors on the propagation of die crack was determined through a design of experiment approach consisting of the definitive screening for initial factor screening, and response surface method through the central composite design. The results have shown that the die thickness, the glass transition temperature, the in-plane CTE of the substrate, and the initial crack length are the factors significantly affecting the die crack propagation in a BGA package. Moreover, at critical parameter conditions, the results have identified a critical crack length of 0.02236 mm. The study is aimed to benefit the research, design, development, assembly, and material engineers in the semiconductor industry providing insight to the die crack propagation of a BGA package. © 2020 Elsevier Lt

Aircraft configuration development through surrogate-based robust optimization using a real-coded fuzzy-genetic algorithm

An alternative methodology that views aircraft configuration development from an optimization perspective is proposed. The method hinges on the idea that design requirements can be expressed as objectives and constraints, which in turn can be expressed as functions of design variables that define the aircraft configuration. The resulting model will reflect the inherent complexity of the aircraft and it cannot be expected to be accurate especially at such an early stage of the design process. Considering the nature of the problem and the design variables, a real-coded genetic algorithm is used as the solution tool. Fuzzy logic is used to avoid the unwarranted imposition of crisp criteria on the low-fidelity model. It is also used in the evaluation of fitness of individuals. Moreover, principles of robust design are integrated into the algorithm to mitigate the sensitivity of objectives on unavoidable variations in the design variables without actually eliminating the root causes. Robustness of objectives are accounted for through their respective standard deviations computed using a surrogate as embodied by a quadratic response surface model. Compared to the conventional approach which is sequential, the proposed method is able to synthesize certain design steps and simultaneously determine key design parameters. It is also able to output in a single run not just one but a set of fuzzy-Pareto optimal candidate configurations subject for validation and higher-fidelity analysis in the subsequent phases of the design process. The availability of options increases the success rate, reduces design iterations, and facilitates a faster design process. © 2017 IEEE

Path planning for mobile robots using genetic algorithm and probabilistic roadmap

Mobile robots have been employed extensively in various environments which involve automation and remote monitoring. In order to perform their tasks successfully, navigation from one point to another must be done while avoiding obstacles present in the area. The aim of this study is to demonstrate the efficacy of two approaches in path planning, specifically, probabilistic roadmap (PRM) and genetic algorithm (GA). Two maps, one simple and one complex, were used to compare their performances. In PRM, a map was initially loaded and followed by identifying the number of nodes. Then, initial and final positions were defined. The algorithm, then, generated a network of possible connections of nodes between the initial and final positions. Finally, the algorithm searched this network of connected nodes to return a collision-free path. In GA, a map was also initially loaded followed by selecting the GA parameters. These GA parameters were subjected to explorations as to which set of values will fit the problem. Then, initial and final positions were also defined. Associated cost included the distance or the sum of segments for each of the generated path. Penalties were introduced whenever the generated path involved an obstacle. Results show that both approaches navigated in a collision-free path from the set initial position to the final position within the given environment or map. However, there were observed advantages and disadvantages of each method. GA produces smoother paths which contributes to the ease of navigation of the mobile robots but consumes more processing time which makes it difficult to implement in realtime navigation. On the other hand, PRM produces the possible path in a much lesser amount of time which makes it applicable for more reactive situations but sacrifices smoothness of navigation. The presented advantages and disadvantages of the two approaches show that it is important to select the proper algorithm for path planning suitable for a particular application. © 2017 IEEE