Increasing the Level of Automation in the Forestry Logging Process with Crane Trajectory Planning and Control

Working with forestry machines requires a great deal of training to be sufficiently skilled to operate forestry cranes. In view of this, it would be desirable within the forestry industry to introduce automated motions, such as those seen in robotic arms, to shorten the training time and make the work of the operator easier. Motivated by this fact, we have developed two experimental platforms for testing control systems and motion‐planning algorithms in real time. They correspond to a laboratory setup and a commercial version of a hydraulic manipulator used in forwarder machines. The aim of this article is to present the results of this development by providing an overview of our trajectory‐planning algorithm and motion‐control method, with a subsequent view of the experimental results. For motion control, we design feedback controllers that are able to track reference trajectories based on sensor measurements. Likewise, we provide arguments to design controllers in an open‐loop for machines that lack sensing devices. Relying on the tracking efficiency of these controllers, we design time‐efficient reference trajectories of motions that correspond to logging tasks. To demonstrate performance, we provide an overview of extensive testing done on these machines.     

Modeling,simulation, and optimization of a microalgae biomass drying process

The aim of the present work was to develop a transient mathematical model focused on microalgae biomass drying, considering two phases: solid (wet biomass) and gas (drying air). Mass and thermal energy balances were written for each phase producing a system of ordinary differential equations (ODE). The solution of the ODE set delivers the temperature and air humidity ratio and biomass profiles with respect to time. The numerical results were directly compared with temperature experimental measurements—for both phases—and with the biomass humidity content. Data from experiment 1 were used to carry out the mathematical model adjustment, whereas data from experiment 2 were used for the experimental validation of the model. The model was adjusted by proposing a new correlation for the mass transfer coefficient and by calibrating the heat transfer coefficient. The transient numerical results were in good quantitative and qualitative agreement with the experimental results, ie, within the experimental error bars. Then the experimentally validated mathematical model was utilized to optimize the following parameters: (i) the electric heater power ( ) and the dry air mass flow rate ( ) and (ii) the convection oven length to width ratio (L/W). The goal was to minimize system energy consumption (objective function). The optimization procedure was subject to the following physical constraints: (i) fixed convection oven total volume and (ii) fixed biomass and drying air contact surface area. For the oven original geometry,  = 3.0 kW and  = 9 g s^?1 were numerically found for minimum energy consumption, so that 36.9% and 43.5% energy consumption decreases were obtained, respectively, in comparison with the measurements of experiment 1. Next, the numerical geometric optimization found (L/W)_opt = 9, with and , which was capable to reach a 51.6% energy consumption reduction in comparison with the original system tested in experiment 1. The novelty of this work consists of the development and experimental validation of a physically based microalgae biomass drying mathematical model, ie, instead of using empirical correlations to predict the drying time and temperature profiles and then minimize system energy consumption. Therefore, the results show that it is reasonable to state that the model could be used to design, control, and optimize drying systems with configurations similar to the one analyzed in this study.     

Eco-efficiency and techno-economic analysis for maleic anhydride manufacturing processes

Clean Technologies and Environmental Policy - Maleic anhydride may be obtained from different technological routes, being the selective oxidation of benzene and oxidation of butane the only ones...     

Runtime verification of autopilot systems using a fragment of MTL- $${\int }$$

International Journal on Software Tools for Technology Transfer - Current real-time embedded systems development frameworks lack support for the verification of properties using explicit time where...     

Nutrient Recommendations for Growing-up Milk: A Report of an Expert Panel

Utilization of expert recommendations in the development of food and beverage nutritional profiles represents an opportunity to merge science and food manufacturing to deliver nutritionally optimized products into the marketplace. This report details expert panel guidelines for the design of a nutritional product for children one to six years of age. This interaction demonstrates the essential synergy between academia and food manufacturers in translating nutrient recommendations to food for their delivery to a population. Important factors for such translation are the identification of applicable nutrient recommendations and selection of an appropriate delivery matrix. This report demonstrates the translation of expert nutritional recommendations to a milk-based product for children—one to six years of age.     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

Influence of the type of calcium salt on the cation absorption and firmness of black ripe olives

The preservation of olives intended for processing as black ripe olives must be done in a salt-free environment although it involves the use of calcium addition to retard olive softening. Calcium chloride and calcium lactate were added by up to 1600 mg Ca L⁻¹ (i) in the preservation liquid of olives before darkening and (ii) in the cover packing brine. The highest texture and calcium absorption were found in the latter case. In addition, the sensory characteristics of the final product were not affected by the use of any of the calcium salts studied at concentrations of up to 1600 mg Ca L⁻¹. Calcium chloride and calcium lactate can be employed to obtain higher texture and calcium content in black ripe olives, although it is advisable to add the cation to the cover packing brine of pitted olives rather than to the preservation liquid.