Power Draw Research Articles

On the Energy Behaviors of the Bellman–Ford and Dijkstra Algorithms: A Detailed Empirical Study

The Single-Source Shortest Paths (SSSP) graph problem is a fundamental computation. This study attempted to characterize concretely the energy behaviors of the two primary methods to solve it, the Bellman–Ford and Dijkstra algorithms. The very different interactions of the algorithms with the hardware may have significant implications for energy. The study was motivated by the multidisciplinary nature of the problem. Gaining better insights should help vital applications in many domains. The work used reliable embedded sensors in an HPC-class CPU to collect empirical data for a wide range of sizes for two graph cases: complete as an upper-bound case and moderately dense. The findings confirmed that Dijkstra’s algorithm is drastically more energy efficient, as expected from its decisive time complexity advantage. In terms of power draw, however, Bellman–Ford had an advantage for sizes that fit in the upper parts of the memory hierarchy (up to 2.36 W on average), with a region of near parity in both power draw and total energy budgets. This result correlated with the interaction of lighter logic and graph footprint in memory with the Level 2 cache. It should be significant for applications that rely on solving a lot of small instances since Bellman–Ford is more general and is easier to implement. It also suggests implications for the design and parallelization of the algorithms when efficiency in power draw is in mind.

Addressing the limitation of Operational Flexibility in Power Generation with Pumped Hydro Energy Storage System – Indian Perspective

The global climate change scenario has impacted and taken corrective measures in shifting the sourcing of primary energy and efficient end-use pattern with more focus to utilise renewable energy resources to replace the depleting fossil fuels. Electric power generation capacity in the Indian subcontinent has gradually been improved to substantially contribute to intermittent and variable renewable power generation mix up to 40% of grid demand. However, the injection of grid-integrated renewable power as per the availability of generation potential will necessitate limiting the power generation from generating plants running on fossil fuel. But the operational flexibility of fossil fuel power plants is mostly designed to run efficiently with a 55% minimum technical load on the machines. In the above situation, stable grid availability and operation with a desirable spinning reserve may be taken care of suitably with an on-grid energy storage system, and in the Indian context, a pumped hydro energy storage system may play a critical role to take care of grid operational variability with adequate measures on frequency and voltage correction through periodic power generation and power draw. In this paper, the feasibility of a pumped hydro storage system from an Indian power sector perspective has been reviewed about the present national power scenario.

Discrete Element Modeling of the Breakage of Single Polyhedral Particles in the Rotary Offset Crusher

Innovation in comminution is expected to continue unabated to address the inefficiencies that are inherent in comminution circuits. The rotary offset crusher (ROC) is a new comminution device with a promising performance potential in terms of throughput due to the enhanced speed of transportation induced by the centrifugal force of the discs. However, the processes driving the comminution of particles trapped in the conical space between the two discs of the crusher are not fully understood. To gain a better insight into the comminution process in this device, discrete element modeling (DEM) simulations were conducted to study the breakage of a single particle for the crusher operated under two different dynamic conditions, i.e., (1) a stationary top disc and (2) both discs rotating at the same speed. For both scenarios, the speed of the discs was varied between 550 and 2350 rpm. Experimental testwork was also conducted with the laboratory prototype to generate the data that were used to calibrate the breakage parameters of the Ab × t10 breakage model. Simulations were performed using polyhedral UG2 ore particles that were generated with the in-built particle generator in the DEM simulator. The simulated ROC, which is operated with both discs rotating, outperformed the ROC with a stationary top disc in terms of the specific input energy and throughput. The crusher with a stationary top disc is characterized by high shear forces (suggesting a higher wear rate), specific input energies greater than 1 kWh/t, and low throughputs (<50 kg/h). The ROC operated with a stationary disc is not recommended for hard rock applications due to expected excessive wear of crushing surfaces and higher energy consumption. The freewheeling discs are recommended, but there is scope to optimize the crusher performance in terms of the power draw, size reduction, and throughput by manipulating the difference between the speeds of the discs. There is also scope to optimize the crusher performance when it is simulated with many particles. Once the full performance potential of the ROC is established, it will then be important to benchmark it against the existing crushers in the minerals industry as well as other industries where crushers are used.

Advances in granular flow modeling: GPU-based multi-sphere DEM approach and tumbling mill dynamics

Modeling irregular-shaped particulate flow systems poses challenges, especially in mineral processing where accurate representation of particle shape is crucial for capturing macroscopic behavior. The multi-sphere approach, employing DEM, addresses this by approximating particles as overlapping spheres within GPU computing. This study validates particle flow dynamics using literature-based data, showcasing the model’s fidelity and GPU advantages. However, calibration and shape approximation limitations require careful consideration. A quaternion-based orientation method enhances DEM and validates experimental data from packing, hoppers, and tumbling mills. Simulations reveal how particle shape influences granular dynamics, affecting lift-off tendencies, responses to lifter face angles, and power draw trends with mill speed changes. Cubical and spherical particles exhibit orderly packing behavior, contrasting with irregular shapes. This investigation underscores the significant role of particle shape in granular dynamics and its implications for industrial processes.

Segregation in binary and polydisperse stirred media mills and its role on grinding effectiveness

Most industrial vertical stirred mills contain a non-uniform set of grinding media sizes. However, this fact is often ignored in simulations, which mostly use monodispersed media. The paper explores the fundamental dynamics of vertical mills when using multiple sizes of grinding media, by using DEM simulation. Our results suggest that by including both large and small media, one may be able to optimise its performance in several manners. The energy going into the contacts can be increased by including a second size, leading to more effective grinding. Including smaller media can also reduce the power draw of the mill, increasing the efficiency and sustainability of the mill. Finally, the natural segregation between different sizes creates different types of collision which grind different particle sizes more effectively. Segregation leads to smaller media at the bottom, so continuous processes can be optimised for fine grinding by feeding from the top, where the larger media are. This further enables control of the resulting product specifications.

Performance Envelope of a 538-Series High-Speed Helico-Axial Pump for High-Gas-Volume-Fraction Operation

Summary Operating at high speeds can have the benefit of increasing the capability of pumps to enhance surface or downhole production of fluids with high gas volume fraction (GVF). This study presents the performance envelope of a high-speed helico-axial pump (HAP) operating at high GVFs (>80%). The ultimate aim of the physical tests was to ascertain the operating capabilities of the pump for potential scaleup to a field prototype. The HAP housing outer diameter was 5.38 in. and operated at a rotational speed of 6,000 rev/min. Air and water were the test fluids, with an average pump intake and a discharge temperature of 28°C. The fluid volume flow rates were varied while maintaining 46 psig at the HAP intake. The liquid and total intake volume flow rates varied from 128 B/D to 664 B/D and 4,941 B/D to 7,593 B/D, respectively. The corresponding dimensionless pressure boost (DPB), GVF, liquid flow coefficients (LFCs), and total flow coefficients (TFCs) were recorded. Additional parameters noted were the percentage of electric current draw to full-load motor current by the HAP motor and the percentage of electric power input to full load power to the HAP motor. The results showed that the HAP had a stable operation during the tests for intake GVF range of 91–98%. The corresponding pump DPB was in the range of 0.0138–0.0751. These values being positive indicated the capability of the HAP to boost fluid pressure even for such high intake gas content and avoid pump gas lock. The results also showed that for a given intake GVF, the HAP DPB increased with decreasing LFC. For a given LFC, the DPB decreased with increasing intake GVF. The percent electric input power to the HAP motor varied between 28% and 64% of full-load motor power. It was observed to strongly increase with decreasing LFC at a given intake GVF and very strongly decrease with increasing intake GVF at a given LFC. The associated percent electric current draw by the HAP motor was seen to vary between 24% and 53% of full-load motor current. Its variation with LFC and intake GVF was similar to those of the percent electric power input. The DPB, percent electric current, and power draw by the HAP motor variations with TFC for a given intake GVF were similar to those of the LFCs. In conclusion, the HAP demonstrated the capability to boost fluid pressure when handling high GVF flows. It is being scaled up to a field prototype to handle higher volume flow rates of high GVF gas-liquid mixtures. This study mainly highlights the method to extend the gas-handling capability of a HAP by operating it at high speeds. Optimal hydraulic design and proper conditioning of the inlet flow components were also incorporated into the HAP architecture. Expanding the HAP operating envelope to handle high-GVF flows significantly unlocks the potential for field operators to maximize hydrocarbon production from high-gas content applications. This, in turn, increases the economic bottom line from the field asset.

Interventional Imaging Systems in Radiology, Cardiology, and Urology: Energy Consumption, Carbon Emissions, and Electricity Costs.

BACKGROUND. The energy demand of interventional imaging systems has historically been estimated using manufacturer-provided specifications rather than directly measured. OBJECTIVE. The purpose of this study was to investigate the energy consumption of interventional imaging systems and estimate potential savings in the carbon emissions and electricity costs of such systems through hypothetical operational adjustments. METHODS. An interventional radiology suite, neurointerventional suite, radiology fluoroscopy unit, two cardiology laboratories, and two urology fluoroscopy units were equipped with power sensors. Power measurement logs were extracted for a single 4-week period for each radiology and cardiology system (all between June 1, 2022, and November 28, 2022) and for the 2-week period from July 31, 2023, to August 13, 2023, for each urology system. Power statuses, procedure time stamps, and fluoroscopy times were extracted from various sources. System activity was divided into off, idle (no patient in room), active (patient in room for procedure), and net-imaging (active fluoroscopic image acquisition) states. Projected annual energy consumption was calculated. Potential annual savings in carbon emissions and electricity costs through hypothetical operational adjustments were estimated using published values for Switzerland. RESULTS. Across the seven systems, the mean power draw was 0.3-1.1, 0.7-7.4, 0.9-7.6, and 1.9-12.5 kW in the off, idle, active, and net-imaging states, respectively. Across systems, the off state, in comparison with the idle state, showed a decrease in the mean power draw of 0.2-6.9 kW (relative decrease, 22.2-93.2%). The systems had a combined projected annual energy consumption of 115,684 kWh (range, 3646-26,576 kWh per system). The systems' combined projected energy consumption occurring outside the net-imaging state accounted for 93.3% (107,978/115,684 kWh) of projected total energy consumption (range, 89.2-99.4% per system). A hypothetical operational adjustment whereby all systems would be switched from the idle state to the off state overnight and on weekends (versus being operated in idle mode 24 hours a day, 7 days a week) would yield the following potential annual savings: for energy consumption, 144,640 kWh; for carbon emissions, 18.6 metric tons of CO2 equivalent; and for electricity costs, US$37,896. CONCLUSION. Interventional imaging systems are energy intensive, having high consumption outside of image acquisition periods. CLINICAL IMPACT. Strategic operational adjustments (e.g., powering down idle systems) can substantially decrease the carbon emissions and electricity costs of interventional imaging systems.

Left ventricular assist devices: yesterday, today, and tomorrow.

The shortcomings of expense, power requirements, infection, durability, size, and blood trauma of current durable LVADs have been recognized for many years. The LVADs of tomorrow aspire to be fully implantable, durable, mitigate infectious risk, mimic the pulsatile nature of the native cardiac cycle, as well as minimize bleeding and thrombosis. Power draw, battery cycle lifespan and trans-cutaneous energy transmission remain barriers to completely implantable systems. Potential solutions include decreases in pump electrical draw, improving battery lifecycle technology and better trans-cutaneous energy transmission, potentially from Free-range Resonant Electrical Energy Delivery. In this review, we briefly discuss the history of LVADs and summarize the LVAD devices in the development pipeline seeking to address these issues.

Extrema-Triggered Conversion for Non-Stationary Signal Acquisition in Wireless Sensor Nodes

While wireless sensor node (WSNs) have proliferated with the rise of the Internet of Things (IoT), uniformly sampled analog–digital converters (ADCs) have traditionally reigned paramount in the signal processing pipeline. The large volume of data generated by uniformly sampled ADCs while capturing most real-world signals, which are highly non-stationary and sparse in information content, considerably strains the power budget of WSNs during data transmission. Given the pressing need for intelligent sampling, this work proposes an extrema pulse generator devised to trigger ADCs at significant signal extrema, thereby curbing the volume of data points collected and transmitted, and mitigating transmission power draw. After providing a comprehensive signal-theoretic rationale, we construct and experimentally validate these circuits on a system-on-chip field-programmable analog array in a 350 nm complementary metal-oxide-semiconductor (MOS) process. Operating within a power range of 4.3–12.3 µW (contingent on the input bandwidth requirements), the extrema pulse generator has proven to be capable of effectively sampling both synthetic and natural signals, achieving significant reductions in data volume and signal reconstruction error. Using a nonideality-resilient reconstruction algorithm, that we develop in this work, experimental comparisons between extrema and uniform sampling show that extrema sampling achieves an 18-fold lower normalized root mean square reconstruction error for a quadratic chirp signal, despite requiring 5-fold fewer sample points. Similar improvements in both the reconstruction error and effective sampling rate objectives are found experimentally for an electrocardiogram signal. Using both theoretical and experimental methods, this work demonstrates the potential of extrema-triggered systems for extending Pareto frontiers in modern, resource-constrained sensing scenarios.

Electric Assisted Hybrid Vehicle

Abstract: This is a concept realised were a fuel driven vehicles can be operated in Parallel Hybrid mode. This will be a continuous parallel operation. Power delivered to the wheels in parallel to the fuel drive is adjustable. Fuel delivered power and Electric delivered power are paralleled at the wheels. Drive Battery for the run is a smaller capacity and can cater few Kilo meters of run. This drive battery is trickle charged with the power and the to the capacity of the engines idling condition and maximum power from the engine survival RPM. This is to counter hampering circulated power draw from the engine. Brakes applied puts the motor in generator mode loading the wheels to charge the drive battery. Motive of this concept is to increase the mileage of fuel driven vehicle

Precise realtime current consumption measurement in IoT TestBed.

The Internet of Things, similar to wireless sensor networks, has been integrated into the daily life of almost everyone. These wearable, stationary, or mobile devices are in multiple locations, collecting data or monitoring and executing certain tasks. Some can monitor environmental values and interact with the environment, while others are used for data collection, entertainment, or even lifesaving. To achieve the wireless part of the system, the majority of sensor nodes are designed to be battery-powered. While battery power has become increasingly ubiquitous, it tends to increase the global carbon footprint of electronic devices. This issue can be mitigated by employing some form of energy harvesting so that batteries can be refilled and the gadget lasts longer, but this does not alter the reality that batteries are still used and eventually discarded. In this paper, the authors emphasize the significance of power consumption in battery-powered devices. To be able to monitor devices' power consumption, one of the measurable parameters is current. When users know the exact current consumption, they can decrease it by polishing the program or tweaking the duty cycle, making radio transmit fewer data or less frequently, thus decreasing overall power draw. In order to simplify current consumption monitoring, the authors have developed a testbed facility that provides real-time current consumption measurements, which may be used to enhance the duty cycle and battery life of the aforementioned devices. While minimizing total current consumption is a great way to extend the battery life and, thus, the carbon footprint, the primary culprit in the Internet of Things is radio communications. This transmission is the primary source of current consumption. By determining the exact amount of current drawn during transmission and adjusting it, users can significantly extend battery life.

Completion Design to Prevent the Production of Sand in a Well by Using Suitable Equipment

The aim of this paper is to propose a completion design to prevent the production of sand in a well named "X" (for confidential reasons) by using suitable equipment. To reach this goal, the equipment materials (metals and elastomers) must be selected. The downhole gravel pack equipment must be chosen, the well schematic drawn and the installation procedure written. The data used are the well-completion design. The well schematics are drawn by using Power Draw 2019. The results show that the recommended metallurgy is stainless steel with 13 chrome. For this case, the best equipment is the wire-wrapped screen with its slot size ranging from 0.008 to 0.012 inch, and the wash pipe size is 1.66 inch. The gravel pack packer, closing sleeve, gravel pack extension, flapper valve, safety shear joint, blank pipe, screen, snap latch and hydrogenated nitrile butadiene rubber material are selected to meet the scope of work. The total cost of the lower completion operation is 354,200$.

Modelling and optimization of a forced convection heat exchanger for Mars waste heat rejection for power cycle and cryocooling applications

A forced convection heat exchanger for waste heat rejection to the Martian atmosphere is modelled for both a power generation and cryogenic refrigeration application. The modeling is based on existing as well as recent, experimentally validated heat transfer and pressure drop correlations for low-Reynolds-number, moderate-Knudsen number finned tube arrays and represents the first detailed examination of optimal heat exchanger design and performance in this regime. These correlations were developed from heat exchanger performance data taken in Mars-like conditions and are described in a separate, companion work [1]. A heat exchanger model based on the effectiveness-NTU method is developed using these correlations and the heat exchanger geometry is optimized using an open-source solver to minimize the overall system mass for a range of candidate heat exchanger materials and heat transfer rates. The optimal heat exchangers generally consisted of shallow, staggered banks of 1–2 mm diameter tubes and 2–4 mm fin spacing, although in some conditions bare tube arrays were optimal. These geometries tended to be preferred due to their low relative pressure drop, a necessity given the power requirement of producing high pressure head in the thin atmosphere. Curve fits that capture the heat exchanger performance are generated based on the input conditions in order to develop a reduced order model that can be used to integrate the heat exchanger model with an existing recuperated Brayton cycle high-temperature gas cooled reactor (HTGR) power cycle model. This power cycle model is used to determine the impact of using convective heat rejection on optimal system mass and compare this approach to radiative heat rejection. For a 40 kWe power output, the optimal overall cycle mass is found to decrease by 78 % when using convective heat rejection compared to a radiator; this is due to the decreased mass of the heat rejection system as well as the lower optimal rejection temperature which results in a lower recuperator mass. The heat exchanger itself has a mass of only 37 kg, a frontal area of 12.7 m2, and requires 2134 W of fan power to operate. The system is shown to perform well over a wide range of Mars ambient conditions. A high-power liquid Oxygen (LOx) reverse Brayton cryocooler cycle is also modeled to determine the benefit of using convective heat rejection for in-situ resource utilization (ISRU) applications. Across a range of power source options and ambient temperatures, using a convective heat rejection system reduced total system mass by up to 43 % and frontal area by up to 95 % compared to a radiator and also reduced the mass-optimal cycle power draw by increasing the optimal cycle efficiency. However, the magnitude of the mass reduction depends strongly on the specific power of the electrical source; the forced convection system provides only a 7 % improvement over a radiator when coupled with a photovoltaic power source due to the increased power demand of the fan. Overall, this technology offers significant performance advantages over radiative heat rejection systems for waste heat rejection applications on Mars and therefore may significantly benefit future crewed Mars missions.

ESTIMATING THE POWER DRAW OF GRIZZLY FEEDERS USED IN CRUSHING–SCREENING PLANTS THROUGH SOFT COMPUTING ALGORITHMS

In this study, the power draw (P) of several grizzly feeders used in the Turkish Mining Industry (TMI) is investigated by considering the classification and regression tree (CART), random forest (RF) and adaptive neuro-fuzzy inference system (ANFIS) algorithms. For this purpose, a comprehensive field survey is performed to collect quantitative data, including power draw (P) of some grizzly feeders and their working conditions such as feeder width (W), feeder length (L), feeder capacity (Q), and characteristic feed size (F80). Before applying the soft computing methodologies, correlation analyses are performed between the input parameters and the output (P). According to these analyses, it is found that W and L are highly associated with P. On the other hand, Q is moderately correlated with P. Consequently, numerous soft computing models were run to estimate the P of the grizzly feeders. Soft computing analysis results demonstrate no superiority between the performances of RF and CART models. The RF analysis results indicate that the W is necessary for evaluating P for grizzly feeders. On the other hand, the ANFIS-based predictive model is found to be the best tool to estimate varying P values, and it satisfies promising results with a correlation of determination value (R2) of 0.97. It is believed that the findings obtained from the present study can guide relevant engineers in selecting the proper motors propelling grizzly feeders.

High-pressure homogenizer valve design modifications allowing intensified drop breakup without increasing power consumption. I. Optimization of current design-principle

This study uses a model-based design approach (CFD coupled to well-established emulsification correlations) to investigate how to choose the valve dimensions of a high-pressure homogenizer so as to achieve intensified drop breakup without increasing the power consumption. Results show how design modifications influence the thermodynamic efficiency of the homogenizer via two effects, a friction factor and a factor related to how high a dissipation rate of turbulent kinetic energy that the design delivers in the centre of the jet. By simple design modifications, thermodynamic efficiency can be increased by 4 % (compared to a representative contemporary valve design) by decreasing the inlet chamber angle, and an additional 5 % by carefully adjusting the outlet chamber angles so as to ensure that the jet exits the gap free of the wall, but then bends and re-attaches to the wall further downstream. The modifications are predicted to allow for achieving the same resulting drop size but with a 17 % lower power draw, compared to a standard contemporary valve, or alternatively a smaller resulting drop diameter at constant power draw.

Machine Learning Based Foreign Object Detection in Wireless Power Transfer Systems

As power electronics continue to drive advancement in electronic devices by managing the system's energy flow, it is advantageous to examine all areas of energy transfer. Wireless power transfer is one section seeing increased adoption in consumer, industrial, and healthcare applications. This consequently increases the concern for safety, reliability, and efficiency. Power transmission to any unintended load is a primary safety issue in high-power wireless power transfer applications, such as unmanned aircraft systems. Foreign object detection is utilized to selectively transmit power to only the intended target to mitigate this issue. This paper seeks to demonstrate a novel alternative method of using machine learning and artificial intelligence techniques for performing foreign object detection solely on the transmitter portion of the system by only monitoring the TX coil current. This method is first evaluated in SPICE simulation software. Then by transitioning to a closed-loop hardware-based testbed designed to be deployed in unmanned aircraft systems. During the test process for the novel FOD method, the aggregate detection accuracy achieved was 83%, including worst-case misalignment conditions with the RX coil. When the outlying worst-case conditions are excluded, the detection accuracy improves to 97%. To measure the FOD effectiveness in preventing temperature rise in a metallic object, the metallic object was placed at the peak power draw location on the transmitter coil. When the metallic object is left at this location for extended periods, the novel FOD method lowers the maximum temperature reached by 86.8°C when compared to no FOD being active. The primary advantage to this novel method is the simplistic hardware additions, which only require minimal alterations to a WPTS without any FOD implemented. Along with this, implementing this foreign object detection method in the transmitter would mitigate the need for a direct communication link between the receiver and transmitter. As a result, the entire system complexity would decrease, thus increasing the UAS power density while maintaining a safe, effective, and reliable wireless power transmission method.

Scale-Up Investigation of a Pilot and Industrial Scale Semi-Autogenous Mill Using a Particle Scale Model

A particle scale model based on a full two-way coupling of the Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPHs) methods is applied to SAG mills. Motion and collisions of resolved coarser particles within an SAG mill are performed by the DEM component. Fine particles in the feed combine with the water to form a slurry, which is represented by the SPH component of the model. Slurry rheology is controlled by solid loading and fine particle size distribution for each volume of slurry. Transport, dispersion, and grinding of the slurry phase particle size distribution are predicted by solving additional coupled advection–diffusion equations in the SPH component of the model. Grinding of the finer particles in the slurry due to collisions and shear of the coarser particles (rocks and grinding media) is achieved via the inclusion of population balance terms in these equations for each SPH particle. This allows prediction of the transport of both coarser and finer material within the grinding and pulp chambers of an SAG mill, including the discharge performance of the mill. This particle-scale model is used to investigate the relative performance (throughput, product size distribution, resident particle size distribution, net power draw, wear) for an SAG mill at a pilot scale and a 36 ft industrial scale. The 36′ SAG mill considered is a geometrically scaled-up version of the 1.8 m Hardinge pilot scale mill but with a longer belly length, reflecting current SAG mill design preferences. The belly lifters are scaled to a lesser degree with a larger number of lifters used (but still many fewer liners than would typically be used in a large SAG mill based on conventional liner selection rules). The model shows that despite reasonable qualitative similarities, many aspects of the charge structure, slurry transport, coarse particle and slurry discharge through the grates, and the collision energy spectra vary in important ways. This demonstrates that a near purely geometric scale-up of an SAG mill is not sufficient to produce a comparable performance at the two physical mill scales.

Trees made out of fintech: Valuing carbon and accumulating capital in China’s Ant Forest

With China’s increasing international environmental leadership, its emerging models for pursuing environmental goals matter. This article examines one such model, the application of financial technology (“fintech”) to solve environmental problems, through the case of Ant Forest. This highly popular app-based project run by Chinese financial technology giant Ant Group transforms individuals’ green, low-carbon actions into afforestation projects. The article investigates processes of capital accumulation in this project, and finds that Ant Forest’s main innovation, in harnessing fintech, is a techno-epistemological one that defines “green” activities according to political-economic relationships that generate corporate profit but compromise environmental benefit. In models like Ant’s, fintech also shifts processes of value creation, especially in articulation with carbon markets, where nature becomes financialized. This involves multiple linked processes and diverse forms of work to create and realize value. Ant Forest’s entanglements with state power draw attention to the role of the state in sustainability fintech and suggest that fintech capital always works through the state, though in evolving ways.

A comparative study of prediction methods for semi-autogenous grinding mill throughput

The mining industry is experiencing a growing amount of stored production data, yet the full potential of these datasets in process modelling remains unexplored. Semi-autogenous grinding (SAG) mills are extensively used in the grinding circuit of mining plants. Precise prediction of SAG mill throughput can result in significant economic benefits, as it can be utilized for better parameter settings to achieve higher throughputs. Furthermore, the development of an accurate throughput prediction model can assist in informed decision-making for long-term planning. The model’s ability to reveal the overall effect of various inputs and estimate the potential throughput change associated with altering each input, can be helpful for determining whether to invest in altering inputs that are laborious and expensive. Numerous SAG mill models have been investigated in the literature; however, a few studies were aimed at forecasting mill throughput. In this research the most accurate prediction model for SAG mill throughput will be investigated through comparing six machine learning models, including genetic programming, recurrent neural networks, support vector regression, regression trees, random forest regression, and linear regression. To achieve this purpose, a real-world data set comprised of 20,161 records from a gold mining complex in Western Australia is investigated and the effective parameters are identified as SAG mill turning speed, power draw of SAG mill, inlet water, and input particle size. As the data set is in the form of time series, the time-dependent nature of the data is considered for prepossessing, model selection, and final comparison. Specially for the first time in this research, delays in data are investigated and utilized to improve prediction performance. Moreover, hyperparameter tuning is performed to determine the best parameter setting for each model prior to implementation. The comparison results demonstrate that the recurrent neural network is the most accurate prediction model, followed by genetic programming and support vector regression. The genetic programming approach is also able to provide a mathematical equation for the SAG mill throughput prediction, which is highly valued by experts in the industry. Sensitivity analysis revealed that the two factors that most significantly affect SAG mill throughput are turning speed and inlet water. It is anticipated that the SAG mill throughput will rise as the SAG mill turning speed increases and the input water decreases.

Grid-Sim: Simulating Electric Fleet Charging with Renewable Generation and Battery Storage

The inevitable electrification of the sub-Saharan African paratransit system poses substantial threats to an already crippled electricity supply network. The integration of any electric vehicle fleet in this region will require in-depth analyses and understanding of the grid impact due to charging. This allows informative decisions for sufficient planning to be made for the required network infrastructure or the implementation of applicable ‘load-shifting’ techniques. This paper presents Grid-Sim, a software tool that enables comprehensive analysis of the grid impact implications of electrifying vehicle fleets. Grid-Sim is applied to assess the load profiles, energy demand, load-shifting techniques, and associated emissions for two charging stations serving an electrified minibus taxi fleet of 202 vehicles in Johannesburg, South Africa. It is found that the current operation patterns result in a peak grid power draw of 12 kW/taxi, grid-drawn energy of 87.4 kWh/taxi/day, and, subsequently, 93 kg CO2/taxi/day of emissions. However, when using the built-in option of including external batteries and a solar charging station, the average peak power draw reduces by 66%, and both grid-drawn energy and emissions reduce by 58%.