Global material analysis on phosphorus flows, supply horizon and impact assessment on phosphorus depletion

Different problems are daily discuss on environmental aspects such acid rain, eutrophication, global warming and an others problems. Rarely do we find some discussions about phosphorus problematic. Through the years the phosphorus as been a real problem and must be more discussed. On this thesis was done a global material flow analysis of phosphorus, based on data from the year 2004, the production of phosphate rock in that year was 18.9 million tones, almost this amount it was used as fertilizer on the soil and the plants only can uptake, on average, 20% of the input of fertilizer to grow up, the remainder is lost for the phosphorus soil. In the phosphorus soil there is equilibrium between the phosphorus available to uptake from the plants and the phosphorus associate with other compounds, this equilibrium depends of the kind of soil and is related with the soil pH. A reserve inventory was done and we have 15,000 million tones as reserve, the amount that is economical available. The reserve base is estimated in 47,000 million tones. The major reserves can be found in Morocco and Western Sahara, United Sates, China and South Africa. The reserve estimated in 2009 was 15,000 million tone of phosphate rock or 1,963 million tone of P. If every year the mined phosphate rock is around 22 Mt/yr (phosphorus production on 2008 USGS 2009), and each year the consumption of phosphorus increases because of the food demand, the reserves of phosphate rock will be finished in about 90 years, or maybe even less. About the value/impact assessment was done a qualitative analysis, if on the future we don’t have more phosphate rock to produce fertilizers, it is expected a drop on the crops yields, each depends of the kind of the soil and the impact on the humans feed and animal production will not be a relevant problem. We can recovery phosphorus from different waste streams such as ploughing crop residues back into the soil, Food processing plants and food retailers, Human and animal excreta, Meat and bone meal, Manure fibre, Sewage sludge and wastewater. Some of these examples are developed in the paper.

Multi-criteria optimization manipulator trajectory planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions.