Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream.

Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the "fourth industrial revolution" that must be adapted to the particular context and needs of big continuous processes such as oil refineries that have evolved to control paradigms supported by sector-specific technologies where big volumes of operation-driven data are continuously captured from a plethora of sensors. The introduction of Artificial Intelligence techniques can overcome the current limitations of Advanced Control Systems (mainly MPCs) by providing better performance on highly non-linear and complex systems and by operating with a broader scope in terms of signals/data and sub-systems. Moreover, the state of the art of traditional PID/MPC based solutions is showing an asymptotic improvement that requires a disruptive approach in order to reach relevant improvements in terms of efficiency, optimization, maintenance, etc. This paper shows the key aspects in oil refineries to successfully adopt Big Data and Machine Learning solutions that can significantly improve the efficiency and competitiveness of continuous processes.

The costs of replacing coal plant jobs with local instead of distant wind and solar jobs across the United States.

To further a just energy transition, jobs lost at retiring coal plants could be replaced by jobs at wind and solar plants. No research quantifies the feasibility and costs of such an undertaking across the United States. Complicating such an undertaking are workers' place-based preferences that could prevent them from moving long distances, e.g. to high renewable resource regions. We formulate a bottom-up optimization model to quantify the technical feasibility and costs of replacing coal plant jobs with local versus distant jobs in the renewables sector. For the contiguous United States, we find replacing coal generation and employment with local wind and solar investments is feasible. Siting renewables local to instead of distant from retiring coal plants increases replacement costs by 5%-33% across sub-national regions and by $83 billion, or 24%, across the United States. These costs are modest relative to overall energy transition costs.

Virtual reality system in conjunction with neurorobotics and neuroprosthetics for rehabilitation of motor disorders.

Cerebrovascular accidents (CVA) and spinal cord injuries (SCI) are the most common causes of paralysis and paresis with reported prevalence of 12,000 cases per million and 800 cases per million, respectively. Disabilities that follow CVA (hemiplegia) or SCI (paraplegia, tetraplegia) severely impair motor functions (e.g., standing, walking, reaching and grasping) and prevent the affected individuals from healthy-like, full and autonomous participation in daily activities. Our research focuses on the development of a new virtual reality (VR) system combined with wearable neurorobotics (NR), motor-neuroprosthetics (MNP) and brain neuro-machine interface (BNMI) to overcome the major limitations of current rehabilitation solutions.

Body mass data set for 1,317 bird and 270 mammal species from Colombia.

Body mass is one of the most important phenotypic attributes in animal ecology and life history. This trait is widely used in the fields of ecology and macroevolution, since it influences physiology, morphological functions, and a myriad of ecological and social interactions. In this data set, our aim was to gather a comprehensive bird and mammal body mass data set from northern South America. We report body mass, discriminated by sex, for 42,022 individual birds and 7,441 mammals representing 1,317 bird species (69% of Colombia's avifauna) and 270 mammal species (51% of Colombian mammals) from the Neotropics. The data were sourced from vouchers collected between 1942 and 2020 and from individuals captured and released at banding stations over the last two decades for birds (2000-2020) and the last decade for mammals (2010-2020), by 10 research groups and institutions in Colombia. This data set fills gaps identified in other similar databases, as it focuses on northern South America, a highly diverse Neotropical region often underrepresented in morphological data sets. We provide wide taxonomic coverage for studies interested in information both at regional and local scales. There are no copyright restrictions; the present data paper should be appropriately cited when data are used for publication. The authors would appreciate learning about research projects, teaching exercises, and other uses given to this data set and are open to contribute in further collaborations using these data.

A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait.

The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton.

Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles.

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.

Inverse Kinematics for Upper Limb Compound Movement Estimation in Exoskeleton-Assisted Rehabilitation.

Robot-Assisted Rehabilitation (RAR) is relevant for treating patients affected by nervous system injuries (e.g., stroke and spinal cord injury). The accurate estimation of the joint angles of the patient limbs in RAR is critical to assess the patient improvement. The economical prevalent method to estimate the patient posture in Exoskeleton-based RAR is to approximate the limb joint angles with the ones of the Exoskeleton. This approximation is rough since their kinematic structures differ. Motion capture systems (MOCAPs) can improve the estimations, at the expenses of a considerable overload of the therapy setup. Alternatively, the Extended Inverse Kinematics Posture Estimation (EIKPE) computational method models the limb and Exoskeleton as differing parallel kinematic chains. EIKPE has been tested with single DOF movements of the wrist and elbow joints. This paper presents the assessment of EIKPE with elbow-shoulder compound movements (i.e., object prehension). Ground-truth for estimation assessment is obtained from an optical MOCAP (not intended for the treatment stage). The assessment shows EIKPE rendering a good numerical approximation of the actual posture during the compound movement execution, especially for the shoulder joint angles. This work opens the horizon for clinical studies with patient groups, Exoskeleton models, and movements types.

Upper limb posture estimation in robotic and virtual reality-based rehabilitation.

New motor rehabilitation therapies include virtual reality (VR) and robotic technologies. In limb rehabilitation, limb posture is required to (1) provide a limb realistic representation in VR games and (2) assess the patient improvement. When exoskeleton devices are used in the therapy, the measurements of their joint angles cannot be directly used to represent the posture of the patient limb, since the human and exoskeleton kinematic models differ. In response to this shortcoming, we propose a method to estimate the posture of the human limb attached to the exoskeleton. We use the exoskeleton joint angles measurements and the constraints of the exoskeleton on the limb to estimate the human limb joints angles. This paper presents (a) the mathematical formulation and solution to the problem, (b) the implementation of the proposed solution on a commercial exoskeleton system for the upper limb rehabilitation, (c) its integration into a rehabilitation VR game platform, and (d) the quantitative assessment of the method during elbow and wrist analytic training. Results show that this method properly estimates the limb posture to (i) animate avatars that represent the patient in VR games and (ii) obtain kinematic data for the patient assessment during elbow and wrist analytic rehabilitation.