system modeling
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2022 ◽  
Jiajun Wu ◽  
David P. Keller ◽  
Andreas Oschlies

Abstract. In this study we investigate open-ocean macroalgae mariculture and sinking (MOS) as ocean-based carbon dioxide removal (CDR) method. Embedding a macroalgae model into an Earth system model, we simulate macroalgae mariculture in the open-ocean surface layer followed by fast sinking of the carbon-rich macroalgal biomass to the deep seafloor (depth > 3,000 m). We also test the combination of MOS with artificial upwelling (AU), which fertilizes the macroalgae by pumping nutrient-rich deeper water to the surface. The simulations are done under RCP4.5 a moderate emission pathway. When deployed globally between years 2020 and 2100, the simulated CDR potential of MOS is 270 PgC, which is further boosted by AU to 447 PgC. More than half of MOS-sequestered carbon retains in the ocean after cessation at year 2100 until year 3000. The major side effect of MOS on pelagic ecosystems is the reduction of phytoplankton net primary production (PNPP) due to the nutrient competition and canopy shading by macroalgae. MOS shrinks the mid layer oxygen minimum zones (OMZs) by reducing the organic matter export to, and remineralization in, subsurface and intermediate waters, while it creates new OMZs on the seafloor by oxygen consumption from remineralization of sunken biomass. MOS also impacts the global carbon cycle, reduces the atmospheric and terrestrial carbon reservoir when enhancing the ocean carbon reservoir. MOS also enriches the dissolved inorganic carbon in the deep ocean. Effects are mostly reversible after cessation of MOS, though recovery is not complete by year 3000. In a sensitivity experiment without remineralization of sunk MOS biomass, the entire MOS-captured carbon is permanently stored in the ocean, but the lack of remineralized nutrients causes a long-term nutrient decline in the surface layers and thus reduces PNPP. Our results suggest that MOS has a considerable potential as an ocean-based CDR method. However, MOS has inherent side effects on marine ecosystems and biogeochemistry, which will require a careful evaluation beyond this first idealized modeling study.

2022 ◽  
Irving Morgado-González ◽  
Jose Angel Cobos-Murcia ◽  
Marco Antonio Marquez-Vera ◽  
Omar Arturo Dominguez-Ramirez

Abstract This research proposes to obtain a mathematical model that describes the dynamic operation of a brushed DC motor, to obtain a state function considering the electrical, mechanical, and thermal effects of the DC motor. The dynamic evolution of the proposed function is evaluated by simulation using Matlab software, and by applying different values of the step type inputs for the brushed motor excitation employing pulse width modulation (PWM) to obtain a wide range of operations. Experimental results show that the developed state function, provides a reliable approximation to estimate the voltage, armature current, mechanical torque, and temperature of the brushed DC motor, showing an error percentage of 0.2%.

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 106
Yunxia Li ◽  
Lei Li

An automated mechanical transmission (AMT) is proposed as a new soft starter for medium-scale belt conveyors in this paper. The AMT is used to start the belt conveyor and shift gears step by step to make the belt conveyor accelerate softly. Based on analyzing common soft-starting acceleration curves, a segmented belt acceleration curve is proposed as a new soft-starting acceleration curve. By analyzing the AMT soft-starting system, the system modeling is built and the AMT output shaft’s angular acceleration is proposed to be controlled to control the belt acceleration. The AMT soft-starting simulation model is established in the environment of AMESim, and simulation results of the soft-starting process from the first to eighth gear positions are given. The main parameter curves of the AMT soft-starting system including the belt, driving pulley, and AMT output shaft are analyzed. The simulation model can indicate the viscoelastic property of the belt. The simulation results prove that the segmented belt acceleration is appropriate for a medium-scale belt conveyor and provide a theoretical and reasonable basis for using an AMT as a soft starter.

Lixandru Ion-Dănuț

A fundamental premise for understanding and forecasting the functioning of a system, modeling is a method that aids in the development of any phenomena, without question. Marketing modeling is a concept that is seldom utilized in university marketing.

2022 ◽  
pp. 406-428
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

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