#Software for the hardware

Here's a rough unmanicured software idea, to be coded in Python, for determining Carbon credits for biochar production over time

 

NOTE1: competing Carbon credit systems don't take into account 'time' decay of Carbon

NOTE2:For your biochar producing tech, look no further than the 'Rainbow Beeeater' biochar kiln, 'The Big Roo' biochar kiln, 'Kon-Tiki' biochar kiln eg. 'Rolls' and 'Permastove' biochar-producing TLUD stove eg. the 'Permastove' V5

NOTE3: No microtransactions will be used in this 'point-of-sale' (POS) software (just trust with Federal/Government reserves). Getting rich while saving the planet isn't always the way to go (though ecocapitalists might disagree...). Why not create POS software for Carbon biogeochemical sequestration that gathers intelligence and becomes more intelligent over time? As an aside, maybe dumb POS terminals will become a thing of the past aka they can be more than just a glorified calculator and collect business intelligence too (a little Orwellian)!

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*Which tools to use for measurement of C and Si?
With an 'Our Sci' reflectometer/ BFA 'Bionutrient Meter' (a beta reflectometer with a bunch of sensors currently under development) (C + ?PhytOC) OR reflectometer(C) and fluorescent microscopy (PhytOC) (using a microscope set up for flurorescence and a fluroescence-capable camera) scan a biochar sample from a larger batch/quantity of biochar produced OR instead of using PhytOC as a proxy of time, use the Silica reflection. Interesting to note is that PhytOC has an approximate refraction level of 1.4 so it could be measured with a refractometer too. Maybe a 3D scanner that could calculate both the Silica AND Carbon (Si, C, PhytOC)?
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#START OF CODE
scan sample of biochar
output-)input: into a cloud database (eg.AWS, Azure, Cloud, Alibaba):
#These questions can be used to collect additional variable data for the sample database/training data
user input: 'What is the moisture content of the biochar sample?'
user input: 'How many tonnes of  biochar have been produced?'
user input: 'What is the main plant used in making the biochar?'
user input: 'What is the estimated percentage of the main plant used in making the biochar?'
user input: 'What are the other plants used in making the biochar with approximate percentages?'
#run some Pytorch machine learning algorithms based on previous graphs/images of UV to NIR taken via #reflectometer (or whichever tool is found most effective and versatile) to determine Carbon percentage
print ('Carbon percentage of biochar sample')
#run some Pytorch machine learning algorithms based on previous images of PhytOCs taken via reflectometer #(or whichever tool is found most effective and versatile) to determine PhytOC percentage
print ('PhytOC percentage of biochar sample')
#A 10,000 year projection or if u were even more optimistic, a million years
print('A graph  as a function of 'PhytOC' with 'Carbon sequestered over the next 10,000 years')
calculate: 'C credits (tonne.year(s)) earned over the next 10,000 years' * 'current market cost of tonne.year'
#(in US/Euro/local currency)
print ('Money earned for biochar produced')
#END OF CODE
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Any ideas or comments??
Any Python programmers interested in working on this code?
Let's build a sustainable and ethical future!
I can be contacted on my 'Contact' page...
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REFERENCES

*'Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis condions' by Zhu, Xinze et el (2019). This prediction could be bootstrapped onto the POS software to make a financial quote based on how many tonne.years of Carbon could be produced based on 'biomass characteristics and pyrolysis conditions'. They used the random-forest ML algorithm from Python's scikit-learn module

*'Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae
(Poaceae)' by Zhe Cai and Song Ge (2017). This paper quotes some very high percentages of phytoliths recognised with the SVM ML algorithm

*'Phytoliths as proxies of the past', by Irfan Rashid (2019). Great overview of phytoliths using examples mostly from fields of archeology

*'Role of phytolith occluded carbon of crop plants for enhancing soil carbon sequestration in agro-ecosystems' by Rajendiran, S. et al (2012). A little dated but great background reading.

*'Phytolith Formation in Plants: From Soil to Cell' by Nawaz, M. et al (2019). Long article, haven't finished reading it yet but very interesting so far...