scholarly journals 2D AND 3D PLOTTER USING ARDUINO NANO

Author(s):  
Jagadeesh Kumar Chougala ◽  
Kirankumar S Patil ◽  
Jayanth H S ◽  
Santhosh M P ◽  
Chaitanya L

This paper aims to develop and implement a low cost and high accuracy mini-CNC machine for 2D and 3D plotting using Arduino Nano. This system can be widely used in small-scale engraving or Image plotting applications where the accuracy and speed are the main performance parameters.

In this work, we fetch the current trends in industrial automation and data exchange technology adopted in Computer Numerical Control (CNC) machine and mitigate the features in in a cost-effective manner. The current trend is Industry 4.0, uses cloud-based systems for information and data exchanges in machine to machine communication. This methodology is reliable, but expensive and can be afforded only by large scale companies. In order to provide the data transparencies at low cost, we utilize a low-cost computing system using Python language for small scale industry. This technique was implemented in the existing CNC machine and the machine parameters such as Machine Operating Mode, Cycle Time, Part Count, Feed rate, Spindle Running Hours, Machine Running Hours, and Machine Utilization Hours are monitored. Graphical user interface (GUI) screens are developed to help human machine interface. Acquired real-time machine data will help boost transparency and help the operator/ user for smart decision making. The IIoT (Industrial Internet of Things) technology helps to connect more numbers of such machines, results in increased machine utilization and productivity through continuously monitoring and analyzes.


2012 ◽  
Vol 44 (2) ◽  
pp. 75-93
Author(s):  
Peter Mortensen

This essay takes its cue from second-wave ecocriticism and from recent scholarly interest in the “appropriate technology” movement that evolved during the 1960s and 1970s in California and elsewhere. “Appropriate technology” (or AT) refers to a loosely-knit group of writers, engineers and designers active in the years around 1970, and more generally to the counterculture’s promotion, development and application of technologies that were small-scale, low-cost, user-friendly, human-empowering and environmentally sound. Focusing on two roughly contemporary but now largely forgotten American texts Sidney Goldfarb’s lyric poem “Solar-Heated-Rhombic-Dodecahedron” (1969) and Gurney Norman’s novel Divine Right’s Trip (1971)—I consider how “hip” literary writers contributed to eco-technological discourse and argue for the 1960s counterculture’s relevance to present-day ecological concerns. Goldfarb’s and Norman’s texts interest me because they conceptualize iconic 1960s technologies—especially the Buckminster Fuller-inspired geodesic dome and the Volkswagen van—not as inherently alienating machines but as tools of profound individual, social and environmental transformation. Synthesizing antimodernist back-to-nature desires with modernist enthusiasm for (certain kinds of) machinery, these texts adumbrate a humanity- and modernity-centered post-wilderness model of environmentalism that resonates with the dilemmas that we face in our increasingly resource-impoverished, rapidly warming and densely populated world.


Author(s):  
Christian Frilund ◽  
Esa Kurkela ◽  
Ilkka Hiltunen

AbstractFor the realization of small-scale biomass-to-liquid (BTL) processes, low-cost syngas cleaning remains a major obstacle, and for this reason a simplified gas ultracleaning process is being developed. In this study, a low- to medium-temperature final gas cleaning process based on adsorption and organic solvent-free scrubbing methods was coupled to a pilot-scale staged fixed-bed gasification facility including hot filtration and catalytic reforming steps for extended duration gas cleaning tests for the generation of ultraclean syngas. The final gas cleaning process purified syngas from woody and agricultural biomass origin to a degree suitable for catalytic synthesis. The gas contained up to 3000 ppm of ammonia, 1300 ppm of benzene, 200 ppm of hydrogen sulfide, 10 ppm of carbonyl sulfide, and 5 ppm of hydrogen cyanide. Post-run characterization displayed that the accumulation of impurities on the Cu-based deoxygenation catalyst (TOS 105 h) did not occur, demonstrating that effective main impurity removal was achieved in the first two steps: acidic water scrubbing (AWC) and adsorption by activated carbons (AR). In the final test campaign, a comprehensive multipoint gas analysis confirmed that ammonia was fully removed by the scrubbing step, and benzene and H2S were fully removed by the subsequent activated carbon beds. The activated carbons achieved > 90% removal of up to 100 ppm of COS and 5 ppm of HCN in the syngas. These results provide insights into the adsorption affinity of activated carbons in a complex impurity matrix, which would be arduous to replicate in laboratory conditions.


2020 ◽  
Author(s):  
Derek Schulte ◽  
Kyam Krieger ◽  
Carl W. Chin ◽  
Alexander Sonn
Keyword(s):  
Low Cost ◽  

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 179
Author(s):  
Said Munir ◽  
Martin Mayfield ◽  
Daniel Coca

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.


Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.


2018 ◽  
Author(s):  
Gonzalo Duró ◽  
Alessandra Crosato ◽  
Maarten G. Kleinhans ◽  
Wim S. J. Uijttewaal

Abstract. Diverse methods are currently available to measure river bank erosion at broad-ranging temporal and spatial scales. Yet, no technique provides low-cost and high-resolution to survey small-scale bank processes along a river reach. We investigate the capabilities of Structure-from-Motion photogrammetry applied with imagery from an Unmanned Aerial Vehicle (UAV) to describe the evolution of riverbank profiles in middle-size rivers. The bank erosion cycle is used as a reference to assess the applicability of different techniques. We surveyed 1.2 km of a restored bank of the Meuse River eight times within a year, combining different photograph perspectives and overlaps to identify an efficient UAV flight to monitor banks. The accuracy of the Digital Surface Models (DSMs) was evaluated compared with RTK GPS points and an Airborne Laser Scanning (ALS) of the whole reach. An oblique perspective with eight photo overlaps was sufficient to achieve the highest relative precision to observation distance of ~1:1400, with 10 cm error range. A complementary nadiral view increased coverage behind bank toe vegetation. The DSM and ALS had comparable accuracies except on banks, where the latter overestimates elevations. Sequential DSMs captured signatures of the erosion cycle such as mass failures, slump-block deposition, and bank undermining. Although this technique requires low water levels and banks without dense vegetation, it is a low-cost method to survey reach-scale riverbanks in sufficient resolution to quantify bank retreat and identify morphological features of the bank failure and erosion processes.


2016 ◽  
Author(s):  
A. Ribeiro ◽  
C. Vilarinho ◽  
J. Araújo ◽  
J. Carvalho

The increasing of world population, industrialization and global consuming, existing market products existed in the along with diversification of raw materials, are responsible for an exponential increase of wastes. This scenario represents loss of resources and ultimately causes air, soils and water pollution. Therefore, proper waste management is currently one of the major challenges faced by modern societies. Textile industries represents, in Portugal, almost 10% of total productive transforming sector and 19% of total employments in the sector composed by almost 7.000 companies. One of the main environmental problems of textile industries is the production of significant quantities of wastes from its different processing steps. According to the Portuguese Institute of Statistics (INE) these industries produce almost 500.000 tons of wastes each year, with the textile cotton waste (TCW) being the most expressive. It was estimated that 4.000 tons of TCW are produced each year in Portugal. In this work an integrated TCW valorisation procedure was evaluated, firstly by its thermal and energetic valorisation with slow pyrolysis followed by the utilization of biochar by-product, in lead and chromium synthetic wastewater decontamination. Pyrolysis experiments were conducted in a small scale rotating pyrolysis reactor with 0.1 m3 of total capacity. Results of pyrolysis experiments showed the formation of 0,241 m3 of biogas for each kilogram of TCW. Results also demonstrated that the biogas is mostly composed by hydrogen (22%), methane (14 %), carbon monoxide (20%) and carbon dioxide (12%), which represents a total high calorific value of 12.3 MJ/Nm3. Regarding biochar, results of elemental analysis demonstrated a high percentage of carbon driving its use as low cost adsorbent. Adsorption experiments were conducted with lead and chromium synthetic wastewaters (25, 50 and 100 mg L−1) in batch vessels with controlled pH. It was evaluated the behaviour of adsorption capacity and removal rate of each metal during 120 minutes of contact time using 5, 10 and 50 g L−1 of adsorbent dosage. Results indicated high affinity of adsorbent with each tested metal with 78% of removal rate in chromium and 95% in lead experiments. This suggests that biochar from TCW pyrolysis may be appropriated to wastewaters treatment, with high contents of heavy metals and it can be an effective alternative to activated carbon.


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