RESUMEN
The use of powdered activated carbon (PAC) is a common process in advanced wastewater treatment to remove micropollutants. Retention and separation of PAC is essential as PAC loaded with micropollutants should not be released into the environment. Determining the activated carbon (AC) residual in the effluent poses a challenge, as there is currently no on-line measurement method. In this study, the correlation between turbidity, measured by scattered light, and absorption at wavelength of 550 nm (Absorption550 nm), measured by transmitted light, was investigated in relation to the AC residue. Linear correlations for turbidity (R2 = 0.95) and Absorption550 nm (R2 = 1.00) to AC concentrations were observed in both laboratory and full-scale experiments in a pilot plant where superfine PAC was added prior to Pile Cloth Media Filtration (PCMF). Decreasing the particle size (d50) while maintaining the same AC concentration leads to increased turbidity: Therefore, a fourfold reduction in d50 results in a 2- to 3-fold increase in turbidity, whereas a 30-fold reduction in d50 leads to a 6-to 8-fold increase. Furthermore, the original wastewater turbidity led to a parallel shift in the linear correlation between turbidity and AC. Coagulant doses of up to 400 mg Me3+/g AC resulted in a 50% reduction in turbidity. However, higher concentrations from 400 to 1,000 mg Me3+/g AC resulted in increased turbidity with only a 30% reduction compared to the initial turbidity. The study also highlights the significance of AC particle size in optical measurements, impacting result accuracy.
RESUMEN
Cognitively challenging training sessions during robot-assisted gait training after stroke were shown to be key requirements for the success of rehabilitation. Despite a broad variability of cognitive impairments amongst the stroke population, current rehabilitation environments do not adapt to the cognitive capabilities of the patient, as cognitive load cannot be objectively assessed in real-time. We provided healthy subjects and stroke patients with a virtual task during robot-assisted gait training, which allowed modulating cognitive load by adapting the difficulty level of the task. We quantified the cognitive load of stroke patients by using psychophysiological measurements and performance data. In open-loop experiments with healthy subjects and stroke patients, we obtained training data for a linear, adaptive classifier that estimated the current cognitive load of patients in real-time. We verified our classification results via questionnaires and obtained 88% correct classification in healthy subjects and 75% in patients. Using the pre-trained, adaptive classifier, we closed the cognitive control loop around healthy subjects and stroke patients by automatically adapting the difficulty level of the virtual task in real-time such that patients were neither cognitively overloaded nor under-challenged.
Asunto(s)
Cognición/fisiología , Trastornos Neurológicos de la Marcha/rehabilitación , Marcha/fisiología , Robótica , Adaptación Psicológica/fisiología , Adulto , Anciano , Algoritmos , Sistemas de Computación , Computadores , Bases de Datos Factuales , Terapia por Ejercicio/métodos , Femenino , Trastornos Neurológicos de la Marcha/fisiopatología , Respuesta Galvánica de la Piel/fisiología , Frecuencia Cardíaca/fisiología , Humanos , Modelos Lineales , Masculino , Persona de Mediana Edad , Esfuerzo Físico , Desempeño Psicomotor/fisiología , Frecuencia Respiratoria/fisiología , Temperatura Cutánea/fisiología , Rehabilitación de Accidente Cerebrovascular , Encuestas y Cuestionarios , Interfaz Usuario-Computador , Caminata/fisiologíaRESUMEN
We implemented a model for prediction of heart rate during Lokomat walking. Using this model, we can predict potential overstressing of the patient and adapt the physical load accordingly. Current models for treadmill based heart rate control neglect the fact that the interaction torques between Lokomat and human can have a significant effect on heart rate. Tests with five healthy subjects lead to a model of sixth order with walking speed and power expenditure as inputs and heart rate prediction as output. Recordings with five different subjects were used for model validation. Future work includes model identification and predictive heart rate control with spinal cord injured and stroke patients.