ENERGY AND NUTRIENT RECOVERY FROM ANAEROBIC TREATMENT OF ORGANIC WASTES

The objective of the research was to develop a complete systems design and predictive model framework of a series of linked processes capable of providing treatment of landfill leachate while simultaneously recovering nutrients and bioenergy from the waste inputs. This proposed process includes an \u27Ammonia Recovery Process\u27 (ARP) consisting of: 1) ammonia de-sorption requiring leachate pH adjustment with lime or sodium hydroxide addition followed by, 2) ammonia re-absorption into a 6-molar sulfuric acid spray-tower followed by, 3) biological activated sludge treatment of soluble organic residuals (BOD) followed by, 4) high-rate algal post-treatment and finally, 5) an optional anaerobic digestion process for algal and bacterial biomass, and/or supplemental waste fermentation providing the potential for additional nutrient and energy recovery. In addition, the value provided by the waste treatment function of the overall processes, each of the sub-processes would provide valuable co-products offering potential GHG credit through direct fossil-fuel replacement, or replacement of products requiring fossil fuels. These valuable co-products include, 1) ammonium sulfate fertilizer, 2) bacterial biomass, 3) algal biomass providing, high-protein feeds and oils for biodiesel production and, 4) methane bio-fuels. Laboratory and pilot reactors were constructed and operated, providing data supporting the quantification and modeling of the ARP. Growth parameters, and stoichiometric coefficients were determined, allowing for design of the leachate activated sludge treatment sub-component. Laboratory and pilot algal reactors were constructed and operated, and provided data that supported the determination of leachate organic/inorganic-nitrogen ratio, and loading rates, allowing optimum performance of high-rate algal post-treatment. A modular and expandable computer program was developed, which provided a systems model framework capable of predicting individual component and overall performance. The overall systems model software, ENRAT, predicted that a full-scale operation to treat 18,750 L leachate/day would need an Ammonia Recovery process consisting of 88,300 L of total gas transfer column volume, an activated sludge system of 74,417 L, and an algal post treatment raceway of 683 m² (30 cm depth). The ARP would consume 262.5 L/day of 6N sulfuric acid and produce 16.12 kg-N/day ammonium sulfate. The activated sludge system and algal post treatment would produce 900 g-VS/day (or 44.6 L 2% sludge) and 6.83 kg-VS/day (or 341.6 L 2% sludge) of bacterial and algal biomass

Multi-View Reconstruction in-between Known Environments

Abstract—We present a novel multi-view 3D reconstruction algorithm which unifies the advantages of several recent reconstruction approaches. Based on a known environment causing occlusions and on the cameras ' pixel grid discretization, an irregular partitioning of the reconstruction space is chosen. Reconstruction artifacts are rejected by using plausibility checks based on additional information about the objects to be reconstructed. The binary occupancy decision is solely performed in reconstruction space instead of fusing back-projected silhouettes in image space. Hierarchical data structures are used to reconstruct the objects progressively focusing on boundary regions. Thus, the algorithm can be stopped at any time with a certain conservative level of detail. Most parts of the algorithm may be processed in parallel using GPU programming techniques. The main application domain is the surveillance of real environments like in human/robot coexistence and cooperation scenarios

Randomized parallel motion planning for robot manipulators

We present a novel approach to parallel motion planning for robot manipulators in 3D workspaces. The approach is based on a randomized parallel search algorithm and focuses on solving the path planning problem for industrial robot arms working in a reasonably cluttered workspace.The path planning system works in the discretized configuration space, which needs not to be represented explicitly. The parallel search is conducted by a number of rule-based sequential search processes, which work to find a path connecting the initial configuration to the goal via a number of randomly generated subgoal configurations. Since the planning performs only on-line collision tests with proper proximity information without using pre-computed information, the approach is suitable for planning problems with multirobot or dynamic environments. The implementation has been carried out on the parallel virtual machine (PVM) of a cluster of SUN4 workstations and SGI machines. The experimental results have shown that the approach works well for a 6-dof robot arm in a reasonably cluttered environment, and that parallel computation increases the efficiency of motion planning significantly

Smoothing of Piecewise Linear Paths

We present an anytime-capable fast deterministic greedy algorithm for smoothing piecewise linear paths consisting of connected linear segments. With this method, path points with only a small influence on path geometry (i.e. aligned or nearly aligned points) are successively removed. Due to the removal of less important path points, the computational and memory requirements of the paths are reduced and traversing the path is accelerated. Our algorithm can be used in many different applications, e.g. sweeping, path finding, programming-by-demonstration in a virtual environment, or 6D CNC milling. The algorithm handles points with positional and orientational coordinates of arbitrary dimension

Optimal Camera Placement to measure Distances Conservativly Regarding Static and Dynamic Obstacles

In modern production facilities industrial robots and humans are supposed to interact sharing a common working area. In order to avoid collisions, the distances between objects need to be measured conservatively which can be done by a camera network. To estimate the acquired distance, unmodelled objects, e.g., an interacting human, need to be modelled and distinguished from premodelled objects like workbenches or robots by image processing such as the background subtraction method. The quality of such an approach massively depends on the settings of the camera network, that is the positions and orientations of the individual cameras. Of particular interest in this context is the minimization of the error of the distance using the objects modelled by the background subtraction method instead of the real objects. Here, we show how this minimization can be formulated as an abstract optimization problem. Moreover, we state various aspects on the implementation as well as reasons for the selection of a suitable optimization method, analyze the complexity of the proposed method and present a basic version used for extensive experiments.Comment: 9 pages, 10 figure

Point trajectory planning of flexible redundant robot manipulators using genetic algorithms

The paper focuses on the problem of point-to-point trajectory planning for flexible redundant robot manipulators (FRM) in joint space. Compared with irredundant flexible manipulators, a FRM possesses additional possibilities during point-to-point trajectory planning due to its kinematics redundancy. A trajectory planning method to minimize vibration and/or executing time of a point-to-point motion is presented for FRMs based on Genetic Algorithms (GAs). Kinematics redundancy is integrated into the presented method as planning variables. Quadrinomial and quintic polynomial are used to describe the segments that connect the initial, intermediate, and final points in joint space. The trajectory planning of FRM is formulated as a problem of optimization with constraints. A planar FRM with three flexible links is used in simulation. Case studies show that the method is applicable

On-line path planning with optimal C-space discretization

This paper is based on a path planning approach we reported earlier for industrial robot arms with 6 degrees of freedom in an on-line given 3D environment. It has on-line capabilities by searching in an implicit and descrete configuration space and detecting collisions in the Cartesian workspace by distance computation based on the given CAD model. Here, we present different methods for specifying the C-space discretization. Besides the usual uniform and heuristic discretization, we investigate two versions of an optimal discretization for an user-predefined Cartesian resolution. The different methods are experimentally evaluated. Additionally, we provide a set of 3- dimensional benchmark problems for a fair comparison of path planner. For each benchmark, the run-times of our planner are between only 3 and 100 seconds on a Pentium PC with 133 MHz

On-line path planning by heuristic hierarchical search

In this paper, the problem of path planning for robot manipulators with six degrees of freedom in an on-line provided three-dimensional environment is investigated. As a basic approach, the best-first algorithm is used to search in the implicit descrete configuration space. Collisions are detected in the Cartesian workspace by hierarchical distance computation based on the given CAD model. The basic approach is extended by three simple mechanisms and results in a heuristic hierarchical search. This is done by adjusting the stepsize of the search to the distance between the robot and the obstacles. As a first step, we show encouraging experimental results with two degrees of freedom for five typical benchmark problems