The study compared changes in strength and regional muscle hypertrophy between different ranges of motion (ROM) in the knee extension exercise. Forty‐five untrained women were randomized to either a control group or to perform the exercise in one of the following 4 groups (0°=extended knee): Full ROM (FULL_ROM: 100°−30° of knee flexion); Initial Partial ROM (INITIAL_ROM: 100°−65°); Final Partial ROM (FINAL_ROM: 65°−30°); Varied ROM (VAR_ROM: daily alternation between the ROM of INITIAL_ROM and FINAL_ROM). Pre‐ and post‐training assessments included one repetition maximum (1RM) testing in the ROM corresponding to the initial, final and full ROM, and measurement of cross‐sectional areas of the rectus femoris and vastus lateralis muscles at 40%, 50%, 60% and 70% of femur length in regard to regional muscle hypertrophy. Results showed that the INITIAL_ROM group presented a greater relative increase than all groups at 70%, and at 50% and 60% the increases were greater than FINAL_ROM, FULL_ROM, and non‐training control (CON) groups. Moreover, FINAL_ROM group presented similar changes compared to the CON group at 60% and 70%. In regard to 1RM, FINAL_ROM and INITIAL_ROM groups presented greater relative increases at the ROM trained, and no group showed greater increases than VAR_ROM or INITIAL_ROM, regardless the ROM tested. In conclusion, partial ROM training in the initial phase of the knee extension exercise promoted greater relative hypertrophy in certain muscle regions than training in other ROM configurations, and no group promoted a greater 1RM increase than VAR_ROM group, which showed similar 1RM increases in the different ROMs tested.

This paper provides an overview of nonlinear state estimation techniques along with a discussion on the challenges and opportunities for future work in the field. Emphasis is given on Bayesian methods such as moving horizon estimation (MHE) and extended Kalman filter (EKF). A discussion on Bayesian, deterministic, and hybrid methods is provided and examples of each of these methods are listed. An approach for nonlinear state estimation design is included to guide the selection of the nonlinear estimator by the user/practitioner. Some of the current challenges in the field are discussed involving covariance estimation, uncertainty quantification, time-scale multiplicity, bioprocess monitoring, and online implementation. A case study in which MHE and EKF are applied to a batch reactor system is addressed to highlight the challenges of these technologies in terms of performance and computational time. This case study is followed by some possible opportunities for state estimation in the future including the incorporation of more efficient optimization techniques and development of heuristics to streamline the further adoption of MHE.

Addressing climate change constitutes one of the major scientific challenges of this century, and it is widely acknowledged that anthropogenic CO₂ emissions largely contribute to this issue. To achieve the “net-zero” target and keep the rise in global average temperature below 1.5 °C, negative emission technologies must be developed and deployed at a large scale. This study investigates the feasibility of using membranes as direct air capture (DAC) technology to extract CO₂ from atmospheric air to produce low-purity CO₂. In this work, a two-stage hollow fiber membrane module process is designed and modeled using the AVEVA Process Simulation platform to produce a low-purity (≈5%) CO₂ permeate stream. Such low-purity CO₂ streams could have several possible applications such as algae growth, catalytic oxidation, and enhanced oil recovery. An operability analysis is performed by mapping a feasible range of input parameters, which include membrane surface area and membrane performance metrics, to an output set, which consists of CO₂ purity, recovery, and net energy consumption. The base case for this simulation study is generated considering a facilitated transport membrane with high CO₂/N₂ separation performance (CO₂ permeance = 2100 GPU and CO₂/N₂ selectivity = 1100), when tested under DAC conditions. With a constant membrane area, both membranes’ intrinsic performances are found to have a considerable impact on the purity, recovery, and energy consumption. The area of the first module plays a dominant role in determining the recovery, purity, and energy demands, and in fact, increasing the area of the second membrane has a negative impact on the overall energy consumption, without improving the overall purities. The CO₂ capture capacity of DAC units is important for implementation and scale-up. In this context, the performed analysis showed that the m-DAC process could be appropriate as a small-capacity system (0.1–1 Mt/year of air), with reasonable recoveries and overall purity. Finally, a preliminary CO₂ emissions analysis is carried out for the membrane-based DAC process, which leads to the conclusion that the overall energy grid must be powered by renewable sources for the technology to qualify within the negative emissions category.

This contribution describes a novel process systems engineering framework that couples advanced control with sustainability evaluation for the optimization of process operations to minimize environmental impacts associated with products, materials and energy. The implemented control strategy combines a biologically-inspired method with optimal control concepts for finding more sustainable operating trajectories. The sustainability assessment of process operating points is carried out by using the U.S. EPA’s Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) tool that provides scores for the selected indicators in the economic, material efficiency, environmental and energy areas. The indicator scores describe process performance on a sustainability measurement scale, effectively determining which operating point is more sustainable if there are more than several steady states for one specific product manufacturing. Through comparisons between a representative benchmark and the optimal steady states obtained through the implementation of the proposed controller, a systematic decision can be made in terms of whether the implementation of the controller is moving the process towards a more sustainable operation. The effectiveness of the proposed framework is illustrated through a case study of a continuous fermentation process for fuel production, whose material and energy time variation models are characterized by multiple steady states and oscillatory conditions.