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Who do you trust?

An independent, technology service for industry to verify and benchmark their process chemistry. For Chemical Suppliers, Minerals & Mining, Pulp & Paper, Metals, Alumina, Food & Beverage and other Water-Based Process Industries.

Process Variance

Process variance is a result of the interaction of multiple factors or variables. If allowed to drift over time or, suddenly change adversely, it can result in significant losses to the industrial site. Process Chemistry Analytics (PCA) focuses on the industrial process chemistry, whilst also considering the effect of other production factors. Some important factors are changes in chemical treatment, personnel or supplier. Changes in plant, raw materials, water quality, equipment, control strategies, chemical addition points, dilution, hydrodynamics and other operational factors.

Whether it is a mine site, a paper mill, metals, alumina, a food or beverage plant or, other water-based industrial site, this process chemistry analytics service assists in separating out the effects of your process chemistry from the many other production and mechanical factors.

In order to make decisions with confidence, management of industrial sites need reliable data with actionable conclusions that can be trusted. PCA is independent of any chemical supplier and provides an impartial assessment. PCA applies industry process chemistry knowledge, advanced data management, statistical and graphical techniques to verify and benchmark your process chemistry and summarise results in a PCA report.

All PCA reports are based on the site’s own production data, where permission has been given to use such data, whether from online process control sensors or manually collected data sets. High security cryptography is used to prove a PCA report originated from PCA and has not been altered by a third party (digitally signed SHA-256 checksum, as designed by the United States National Security Agency – the same technique that software distributors use to verify the integrity of file downloads).

Therefore, the client can have confidence it is the original report from PCA, based on the industrial site’s own data.

Service Reports for Chemical Suppliers

Enhanced service reports on your chemical treatment. These can be on a monthly, bimonthly or less frequent basis. For example, for review meetings with your customer. Benefit from this independent service from PCA to demonstrate to your customer that you have confidence in your chemical treatment.

Reports on Plant Trials of New Chemical Treatments

These can be prepared for the chemical supplier or the industrial site or both. Typically they involve comparing production performance before, during and after the trial. They enable you to reliably quantify the effects of a new chemical treatment.

Benchmarking Reports

These can be prepared for the chemical supplier or the industrial site or both. For example, to compare performance of one industrial process at different times. Or, with permission, to compare performance of a specific chemical treatment at two or more similar industrial sites. What is the industry average performance for a specific chemical treatment? What is the variance in performance?

Trouble Shooting Reports

For those times when the site is unhappy with current performance and needs to diagnose process chemistry issues.

PCA Process

Objectives

In most cases, the aim is to compare different periods of production. These periods need to be defined for some specific process chemistry to be investigated. For example, questions about the effects of a change in process chemistry or control strategy. Based on years of process chemistry consulting experience in industry, the founder of PCA holds discussions with the client to define the objectives and scope of work. Any process chemistry issues that need investigating and the types of process data available at the site are discussed. The specific unit processes to focus on are defined. If the client is concerned about data security, PCA can sign a Non-Disclosure Agreement.

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review

When PCA has an ongoing contract with the client, PCA is able to review and compare fresh production data with historical periods. This enables issues to be identified earlier and is therefore recommended. The founder of PCA is available to discuss each report and the implications for the process chemistry. A database is accumulated over time that can be used to benchmark your process and identify optimum process chemistry parameters. In most cases, we are looking to identify conditions with the best average performance, whilst minimising the process variance. Management can then decide what actions to take to improve process chemistry performance.

data acquisition

The previously defined, relevant process chemistry data is uploaded by the client to PCA’s cloud storage using a customised form in their web browser (if you have slow, unreliable Internet, other methods can be used for sending large data sets). PCA is using the latest, high security Google infrastructure, where data is end-to-end and endpoint encrypted. Only PCA can access the data. Data sharing is controlled by PCA using administrator privileges. Customised data collection forms are available to the client for logging key changes in process chemistry and, the data stored in the PCA cloud ready for analytics reporting.

analytics reporting

Based on the previously defined process chemistry objectives, the data is processed through data management and statistical algorithms to produce a PCA report. This report summarises the process data to highlight statistically significant differences between production periods and process chemistry. The aim is to answer questions by interpreting the data at a deeper level and provide recommendations that management can action. Sometimes, the analytics raises more questions, but the aim is to identify answers, or at least potential answers that can be tested. PCA uses the leading, trusted advanced analytics tools, for example R, Python, Bash, SAS and others. PCA reports are digitally signed to prove they originate from PCA and have not been altered by a third party. The signature can be verified by the client or PCA.

Trust Independent PCA

Get started today by sending your business enquiry. PCA is independent of any chemical supplier and bases its analytics on industry process chemistry knowledge and the industrial site’s own data. Each chemical treatment is assessed in an impartial, objective manner. The founder of PCA has decades of experience in process chemistry technical consulting for multinationals in the Chemical and other industries, such as Minerals & Mining, Pulp & Paper, Metals, Alumina, Food & Beverage. Work directly with the founder of PCA, not an intermediary. Trust in a positive outcome.

Why Choose PCA?

PCA is independent of any chemical supplier and has no interest in selling a particular chemical treatment. That is for the chemical supplier! PCA provides objective assessments of process chemistry performance using industry process chemistry knowledge and the industrial site’s own data, rather than data generated by the chemical supplier. Data you are more likely to trust.

If you are a chemical supplier, utilise this independent service from PCA to demonstrate to your customer that you have confidence in your chemical treatment. If you are an industrial site, utilise this service to verify, benchmark and improve your process chemistry performance.

You work directly with the founder of PCA, not an intermediary. Therefore, you can be assured of getting the best from PCA to ensure the ongoing success of your business. For PCA, that means providing your business with an excellent process chemistry analytics service.

The founder has an unique combination of in-depth industry process chemistry, data analytics and information technology knowledge. Based on decades of experience working for multinationals in technical consulting and Research & Development. He has worked on industrial process chemistry projects in many international locations, from mine sites in remote jungles, deserts and arctic tundra, to paper & board mills, food & beverage, metals and other industrial sites in less remote locations.

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Independent from any chemical supplier

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In-depth industrial process chemistry knowledge

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High trust and data security

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Expert data analytics using the leading advanced analytics tools

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Team approach

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Relevant international industry experience

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A service for chemical suppliers and industrial sites

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Scalable business model

Mission

There is a need in the industrial market place for a trusted, independent, technology service to assist chemical suppliers and industrial sites to improve process chemistry performance. PCA’s mission is to provide your business with that service.

Vision

To be the leading, trusted, go to process chemistry analytics service for companies internationally that want to improve their process chemistry performance.

Values

PCA trusts in industry process chemistry knowledge, information technology, maths and proven algorithms to report objectively on your process chemistry. PCA is committed to applying the above to provide creative, high productivity, cost-effective solutions. PCA has high awareness of and is committed to the environment, health, safety and data security.

Scatterplot comparing Spearman and Pearson correlation 1

Attribution: Skbkekas [CC BY-SA 3.0], via Wikimedia Commons

Mining at the Tarkwa Gold Mine in Ghana

Attribution: Iamgold [CC BY-SA 3.0], via Wikimedia Commons

Scatterplot comparing Pearson and Spearman correlation 2

Attribution: Skbkekas [CC BY-SA 3.0], via Wikimedia Commons

Nippon Paper Industries, Akita Mill

Attribution: 掬茶 [CC BY-SA 4.0], via Wikimedia Commons

Tarkwa Processing Plant, Ghana

Attribution: Iamgold [CC BY-SA 3.0], via Wikimedia Commons

Python Code for Scatterplot comparing Pearson and Spearman correlation 2

Attribution: Skbkekas [CC BY-SA 3.0], via Wikimedia Commons

Scatterplot comparing Spearman and Pearson correlation 3

Attribution: Skbkekas [CC BY-SA 3.0], via Wikimedia Commons

The Standard Normal Probability Distribution with shaded regions

Attribution: D Wells [CC BY-SA 4.0], via Wikimedia Commons

The Grampian Prepared Foods Factory, Industrial Estate Road, UK

Attribution: Eric Jones / The Grampian Prepared Foods Factory, Industrial Estate Road, via Wikimedia Commons

Visualisation of how standard deviation (σ) is calculated

Attribution: Thebiologyprimer [CC BY-SA 4.0], via Wikimedia Commons

Processing facilities at the Essakane Mine in Burkina Faso

Attribution: Iamgold [CC BY-SA 3.0], via Wikimedia Commons

Excess Kurtosis Beta Distribution with mean for full range and variance from 0.05 to 0.25

Attribution: Dr. J. Rodal [CC BY-SA 3.0], via Wikimedia Commons

Mill in Hearst, Ontario, Canada

Attribution: P199 [CC BY-SA 3.0], via Wikimedia Commons

Nyrstar zinc factory near Budel-Dorplein (NL)

Attribution: Apdency [CC0], via Wikimedia Commons

Graph of bias in estimated standard deviation vs. alpha (significance level) and N (sample size)

Attribution: Rb88guy [CC BY-SA 3.0], via Wikimedia Commons

3D representation of a hierarchical tree defining a classification (cluster) performed after a factorial analysis of the multiple correspondences on the "tea" dataset available under R with FactoMineR

Attribution: Jackverr [CC BY-SA 3.0], via Wikimedia Commons

This is based on a print of the “Single Malt Whisky Flavor Map” which was designed by the “Friends of the Classic Malts”, Glasgow, and published in free copies 2007

Dave Broom was named as the Editor. Diageo funded the project.

Attribution: Tasma3197 [CC BY-SA 3.0], via Wikimedia Commons

Cameronbridge Distillery, Scotland. This Diageo distillery is the birthplace of Cameronbridge pure grain whisky, and it is also where Gordons Gin, Smirnoff Vodka and other 'exotic' spirits are distilled

Attribution: James Allan / Cameronbridge Distillery, via Wikimedia Commons

Norbord Factory, Scotland. Manufacturers of engineered wood based panel products

Attribution: Ivor MacKenzie / Norbord Factory, via Wikimedia Commons

Visit of an EITI commission 2008 in Ghana: In the caldera of the Ahafo mine, an open-pit mine of the Newmont Ghana Gold Limited

Attribution: The EITI [CC BY-SA 2.0], via Wikimedia Commons

Box plot description

Attribution: Dcbmariano [CC BY-SA 4.0], via Wikimedia Commons

Processing scheme for the Rosebel Gold Mine in Suriname

Attribution: Iamgold [CC BY-SA 3.0], via Wikimedia Commons

VisIt's Scatter plot allows for the visualisation of multivariate data of up to four dimensions

The Scatter plot takes multiple scalar variables and uses them for different axes in phase space. The different variables are combined to form coordinates in the phase space and they are displayed using glyphs and coloured using another scalar variable.

Attribution: UCRL [Public domain], via Wikimedia Commons

Kemsley Paper Mill, UK

Attribution: Joe White / Kemsley Paper Mill, via Wikimedia Commons

Box-plot of normal and non normal distribution. Created with R

Attribution: Gbdivers [CC BY-SA 3.0], via Wikimedia Commons

The Ravensthorpe Nickel Mine, Western Australia

Attribution: Calistemon [CC BY-SA 3.0], via Wikimedia Commons

An example of the bivariate Gaussian (normal), Student-t, Gumbel, and Clayton copulæ

Attribution: Avraham [CC BY 4.0], via Wikimedia Commons

This is number 5 Blast Furnace located at the Port Talbot Corus (now Tata) Steel Plant, UK

Attribution: Grubb at English Wikipedia [Public domain], via Wikimedia Commons

Data fusion involves techniques that combine data from multiple sources (dimension #1 & #2) and gather that information in order to achieve inferences (see scatter plot)

More effective and potentially more accurate than using a single source of data (one or the other histogram).

Attribution: Jarekt [CC BY-SA 3.0], via Wikimedia Commons

Aluminium smelter of TRIMET Aluminium, Essen, Germany

International Paper Company, Georgetown, USA

Image of random data plus trend, with best-fit line and smoothing curves

The data is 1000 points, with a trend of 1-in-100, with random normal noise of SD 10 superimposed. The r2 fit of the raw data is 0.08; of the 10-pt-smoothed, 0.57; of 100-pt-smoothed, 0.97. For the raw data, the simple trend line explains almost none of the variance of the time series (only 8%). For the 100-pt filtering, the trend line explains almost all of the data (97%). The trend lines are almost identical as are the confidence levels. It shows that a low r2 value should not be interpreted as evidence of lack of trend.

Attribution: Maksim [CC BY-SA 3.0], via Wikimedia Commons

Twin Creeks gold mine, Nevada, USA; Carlin-style mineralisation in Mesozoic sedimentary rocks

Attribution: Geomartin [CC BY-SA 3.0], via Wikimedia Commons

Terra Mineralia. Minerals Exhibition TU Bergakademie Freiberg (Gypsum with Calcite in foreground)

Attribution: Kora27 [CC BY-SA 4.0], via Wikimedia Commons

Occasional updates

What is Process Chemistry Analytics?

It is an effective, innovative service model for chemical suppliers and industrial sites to optimise and improve their process chemistry. Not just cost cutting, but real improvements in productivity and profitability. Some examples of financial savings made by...

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Acknowledgement

PCA would like to thank Wikimedia Commons and the contributors for the rich resource of industry and statistics examples. All are available under the Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) licence -...

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Get Started Today

There is a need in the industrial market place for a trusted, independent, data-based service to assist chemical suppliers and industrial sites to improve process chemistry performance. PCA’s mission is to provide your business with that service.

Get Started Today