Speaker | Transcript |
| Fred | Hello and thank you for inviting me to present at Discovery Summit Americas 2020. My name is Fred Girshick and I'm going to talk about lubricant research using JMP non-linear regression. |
| So my agenda, I'll do some introductions about myself and my technical field, a little bit about the background of lubricants research, |
| the types of questions we want to answer using statistical tools, give some examples of nonlinear models, show how I do a nonlinear analysis, and then talk about conclusions and plans for the future. |
| So introductions, that's the building where I work. |
| So who am I |
| If any of you shouted out 24601, you get extra credit at the final exam. My name is Fred Girshick. I'm a researcher for a specialty chemical company, you can see our logo in the upper right hand. |
| We are a manufacturer of chemical additives for lubricants and fuels. We do have a global statistics group |
| for help with more complicated situations, but we encourage researchers to do their own statistical analysis or get closer to the data, understand better the assumptions and the sources of error. |
| My experience is with various forms of engine oils for reciprocating internal combustion engines |
| passenger car, on-highway trucks, off-highway construction equipment, railroad, aviation, stationary engines. And my specialty for the past 19 years is large engines and large is a relative term. So, later on, I'll show you what I mean when I say a large engine. |
| I was a previous user of SAS. I'm currently user of JMP. I don't consider myself to be a sophisticated user |
| I might be intermediate. I tend to do the same type of analyses over and over again because I'm generally asking the same questions over and over again, I, I'm still learning. I'd like to expand and become more sophisticated. |
| I am better at Microsoft Excel than I am at JMP, so I use Excel to prepare the data set to import into JMP, and I very often export the results to Excel, just because I'm more adept at the graphing tools and then export to PowerPoint for customer presentation or into Word for |
| ....for a technical report. |
| I'm not going to do a real time live action demonstration getting into JMP and going through it. So I just have screenshots and I'll be pointing to now I would do this now, I would do that. |
| engines, transmissions, gears, pumps, motors. Lubricants can be solid, liquid or gas. |
| I concentrate on liquid engine oils. |
| detergent, dispersant, and antioxidant, etc. |
| Now within each of those additive types there are many different chemical options, so detergent isn't one thing, it's a family of things. And of course, not all engine oils contain all additive types. You only use what's needed |
| for efficiency. |
| Today's talk is going to focus on liquid lubricants, engine oils, for reciprocating internal combustion engines (RICE). That's what's generally in your car. |
| detergent, dispersant, antioxidant, anti-wear, friction modifier. One example of each. So detergent might be calcium sulphonate. |
| polyisobutylene succinic anhydride polyamine (PIBSA/PAM). |
| Antioxidant is...might be a hindered phenol. Anti-wear might be zinc dialkyl dithio phosphate (ZDDP) complicated molecule. |
| And friction modifier, something like glycerol dioleate. You'll notice all of these have a more polar part of the molecule and a less polar part of the molecule. |
| In general terms, the polar part is what gives it its function and the non polar part is what makes it soluble in petroleum oil. |
| And now I'll tell you what I mean by large engines. Here's an example of one of our laboratories |
| So large engines. First, what is not a large engine. So this is a car. It's my wife's car. It has a reasonably powerful engine of 200 horsepower. |
| That's not a large engine. What about trucks going down the highway, you know, making power, pulling freight. No, they are about 500 horsepower. That's not a large engine. My examples of large engine is a railroad locomotive engine. |
| So here's a picture of a locomotive engine being installed in locomotive. The red outline is the engine and there is the person who's installing it, gives you an idea of the size. |
| Stationary natural gas engines, which today's talk is going to be about. |
| Just get my laser pointer. |
| So here's an example of one of those 5900 horsepower. The green thing. And then at the end is the person working on it. |
| And another example of a large engine is a marine engine, ships at sea. And here's a marine engine. This particular one generates 98,000 horsepower and there is the person working on it. So this is what I mean when I say large engines. They really are large. |
| The types of questions we want to answer in lubricants research, things like how well does this bench test predict real real-world performance, you know, if I'm doing screener tests, rig tests. |
| How does performance depend on concentration of an additive? How much do I need to put in? How does the structure of the additive effect performance? If I make that hydrocarbon chain longer or more branched? If I change the ratio of the polar part to the non polar part? |
| Can I predict performance from composition, just looking at the molecule can I predict what it will do? |
| How long will the product last before it needs to be changed? So in your car, you're told to change your oil every 3000 miles or 5000 or 7500 or 10,000, depending who you ask. |
| How much better is my premium product than my main line product? Is there a differentiation between those? Is there a value proposition? How does my product compare to my competitor's product? So, these are the sorts of things we do. |
| Now, let me talk about nonlinear. What do I mean by nonlinear. So in the equation world, in the statistics world, nonlinear means nonlinear in the parameters, not in the variables. |
| Okay, so let's play a very fast game of linear or no linear... |
| nonlinear. So y equals mx plus b. This is the classic linear equation. |
| If you don't do anything else you know that. |
| Well, what about a quadratic. Now I have x squared. And that's not linear. That's quadratic, but a, b, and c are the parameters. They are linear. That's also a linear equation. |
| What about cubic? Same thing. Any polynomial is going to be linear in the parameters. So it's linear regression. |
| a times e to the bx. So I have a pre exponent...exponential a, and I have b in the exponent, but this equation can be easily rewritten as log and now it's a linear equation again. So I would just regress log of y against x. |
| Y equals A plus B x to the C. So this is not e to the x. It's x to the something. And this is nonlinear. So this is an example of a non linear equation. |
| Y equals A over x plus b. |
| Okay. A and B are in different places. There's a sum, but again I can rewrite this, if I take reciprocals. So I would regress 1 over y against x and then this becomes a linear situation. |
| Y equals A times x over b plus x. |
| This is a nonlinear. This is very classical Michaelis-Menten, something to do with biology and |
| enzyme kinetics. |
| And then here's a sort of complicated equation. This is the equation we're going to be using today. And this is distinctly nonlinear. |
| So the example I've chosen for today is a natural gas engine |
| oxidation, so in service, the oil oxidizes. This is a picture of one of the engines |
| that we did the test in. This is a compressor station. So the blue outline is the engine. The red outline is the compressor that it's driving, the pump, and that rather large structure highlighted in green in the back, that's the radiator. |
| So in general, oxidation for our purposes, oxidation is degradation caused by reaction with oxygen. |
| Now, strictly speaking in chemistry class you learn oxidation can occur without oxygen. It has to do with loss of electrons, but that's not what's going on here. Common examples are when apple slices turn brown or old milk goes sour. |
| As we said before, engine oils are mostly hydrocarbon molecules. They are exposed to high temperatures during engine operation. |
| Fuel combustion generates free radicals that get into the oil and promote oxidation. Free radicals are molecular fragments with unpaired electrons. |
| They are unstable and reactive and they attack other molecules to pair their electrons. During fuel combustion molecules are just blown apart to form these fragments |
| Oxidation of the engine oil leaves to undesirable consequences like oil thickening. So higher viscosity than the engine design needs |
| also lowers fuel economy, because you have to push around a thicker liquid. Oxidation forms acids and acid can corrode metal parts. Oxidation causes deposits and the deposits can block oil passages and starve the engine from lubricating oil or impede moving parts, just sticking things together. |
| Oxidation in our field is often measured by infrared and the units are absorbance per centimeter. Engine manufacturers publish limits at which the oil must be changed. So when oxidation reaches a certain point, you're required to change the oil to maintain your warranty. |
| In the test design I'll be talking about today, there were two different natural gas engine manufacturers, which I've just called X and Y to not name names and make it a generic |
| There were three oils, which I've called blue, red and green, or a, b, and c for this example. And it was a 3x2 design, so each oil was in each engine design. |
| The total...it was run for about 14 months which is 10,000 or 11,000 operating hours. That's about 15 months 14-15 months of continuous operation at greater than 95% of maximum load. |
| The engines were only shut down when it was necessary to do an oil change. |
| Now if you think about that period of time, |
| if you, if you drove your car near its maximum engine output, and let's say average 70 miles an hour, that'd be 700,000 miles of a test. |
| Now for today's example I'm only going to be looking in detail at one of the engine models, the one I've called x. |
| Oil samples were taken every week to 10 days, so there's total of about 600 samples. |
| Many parameters were mentioned about 20 oil properties. |
| Also we examined the engine, made physical measurements of wear, physical measurements of deposits. I'm only going to look at oxidation for now. |
| So here's, here's the data set. It's been truncated. I've simplified it to only show what will need for today. So the variables of color |
| is the oil formulation, the blue, green, and red. Oil is variable, that's the age of the oil in hours. So that's the hours since the last oil change, whereas test is the hours accumulated, the total since the test started, and Ox is oxidation in these units absorbance per centimeter. |
| Now you'll notice the rows, I've color coded the rows according to the color. So all the oil what I've called blue oil, I've colored the markers in blue and red in red, etc. And I've also assigned shapes. So the x manufacturer has a square and the y manufacturer has a circle. |
| Now, as I say, the full data set is much more extensive than this. |
| Okay, so let's take our data set. |
| plot the data. |
| It's always the first thing you do. |
| So in JMP, |
| so what I'll be doing, again, I'm not doing a live JMP demonstration. So I'll show the action |
| On the left hand words and that arrow means it's an action and then there will be a red circle showing where you do it. So use the graph platform, a box comes down, Graph Builder, |
| and that brings you to this box, Graph Builder. |
| So take oil to be the x variable. So you just click on that and drag it down here. |
| And in the absence of a Y, it shows you like a histogram. |
| And then ox is our dependent variable; ox, the y variable and you get this |
| Now there's an obvious outlier. Even I can tell that. We'll get rid of that later. |
| So it looks pretty messy. |
| So I'm going to separate the two different OEMs, original equipment manufacturer. |
| So if I take OEM and put that into overlay, |
| now I have blue and red just JMP picks the colors. So it turns out the blue is the x and red is the y. And you can see the two different engine models behave very differently. |
| Blue obviously has a shorter lifetime; red has a longer lifetime lifetime of oil. So for today, we're only going to look at a... |
