Structural Equation Modeling of Coupled Twin-Distillation Columns (2021-EU-45MP-756)

markus

Markus Schafheutle, Consultant, Business Consulting
Laura Castro-Schilo, JMP Senior Research Statistician Developer, SAS
Christopher Gotwalt, JMP Director of Statistical R&D, SAS

We describe a case study for modeling manufacturing data from a chemical process. The goal of the research was to identify optimal settings for the controllable factors in the manufacturing process, such that quality of the product was kept high while minimizing costs. We used structural equation modeling (SEM) to fit multivariate time series models that captured the complexity of the multivariate associations between the numerous process variables. Using the model-implied covariance matrix from SEM, we then created a Prediction Profiler that enabled estimation of optimal settings for controllable factors. Results were validated by domain experts and by comparing predictions against those of a thermodynamic model. After successful validation, the SEM and Profiler results were tested in the chemical plant with positive outcomes; the optimized predicted settings pointed in the correct direction for optimizing quality and cost. We conclude by outlining the challenges in modeling these data with methodology that is often used in social and behavioral sciences, rather than in engineering.

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Speaker	Transcript
	Hello, I'm Chris Gotwalt and
	today I'm going to be
	presenting with Markus Schafheutle
	and Laura Castro-Schilo
	on an industrial application of
	structural equations models, or
	SEM. This talk showcases one of
	the things I enjoy most about
	my work with JMP. In JMP
	statistical development, we have a
	bird's eye view of what is
	happening in data analysis
	across many fields, which gives
	us the opportunity to cross
	fertilize best practices across
	disciplines.
	In JMP Pro 15, we added a new
	structural equations modeling
	platform. This is the dominant
	data analytic framework in a
	lot of social sciences because
	it flexibly models complex
	relationships in multivariant
	settings. One of the key
	features is that variables may
	be used as both regressors and
	responses at the same time as a
	part of the same model.
	Furthermore, it occurred to me
	that with these complicated
	models they are represented with
	diagrams that, at least on the
	surface, look like diagrams
	representing manufacturing
	processes. I wasn't the only one
	to make this connection. Markus,
	who was working with a
	chemical company, thought the
	same thing. He was working on a
	problem with a chemical company
	with a two column twin
	distillation manufacturing
	process where they wanted to
	minimize energy costs which were
	largely going to steam
	production, while still making
	product that stayed within
	specification. He reached out to
	his JMP sales engineer,
	Martin Demel, who then connected
	Markus to Laura and I.
	We had a series of meetings
	where he showed and described
	the data, the problem and the
	goals of the company. We were
	able to model the data
	remarkably well. Our model was
	validated by sharing the results
	as communicated with the JMP
	profiler to the company's
	internal experts and then with
	the first principle simulator
	and then with new physical data
	from the plant. This was a clear
	success as a data science
	project. However, I would like
	to add a caveat. The success
	required the joint effort of
	Laura, who is a top tier expert
	in structural equations
	modeling. Prior to joining JMP,
	she was faculty in quantitative
	psychology at the University of
	North Carolina, Chapel Hill, one
	of the top departments in the
	US. She is also the inventor of
	the SEM platform in JMP Pro.
	This exercise was challenging
	even for her. She had to write a
	JSL program that itself wrote
	a JSL program that specified
	this model, for example.
	The model we fit was perhaps
	the largest and most
	sophisticated SEM model of all
	time. I want to call out the
	truly outstanding and
	groundbreaking work that
	Laura's done both with the SEM
	platform generally and in this
	case study in particular. Now
	I'm going to hand it over to
	Markus, who is going to give
	background to the problem the
	customer wanted to solve, then
	Laura is going to talk about
	SEM and her model for this
	problem. I'll do a brief
	discussion of how we set up the
	profiler and then Markus will
	wrap up and talk about the
	takeaways from the project and
	the actions that the customer
	took based on our results.
	Thank you, Chris, for the
	introduction.
	Before I start with the problem,
	I want to make you familiar with
	the principles of distillation.
	Distillation is a process which
	separates a mixture of liquids
	most of the time and separates
	them into their individual
	components. So how does this
	work? Here, you see a schematic
	view of a lab distillation
	equipment. You see here is a
	flask where the crude mixture,
	which has to be separated, is
	inside. You heat this up, here
	in this case, with an oil bath
	and stir it and then it starts
	boiling. So the lowest boiling
	material starts first and then the
	vapor goes up here and pauses
	here the thermometer to reach
	the boiling temperature and then
	further goes into these cooler
	here where it condensates and
	then the condensates drop here
	into the other little flask.
	So as I said, it's built
	from...for separating a mixture
	of liquids with different
	boiling points, and those are
	separated by boiling point.
	For example, everybody knows
	that perhaps if you want to make
	schnapps from a mash, you just
	make the mash inside of this
	flask, heat it up, and then you
	distill the alcohol over here
	and get the schnapps.
	As it looks very simple
	here in the lab, in the
	industry it's a bit more
	complicated. Because let's
	say the equipment is not
	only bigger but more
	complex and mainly because
	of of the engineering part
	of of the story.
	So in this study, the
	distillation was not done
	batchwise as you've seen before,
	but in a continuous manner. This
	means the crude mixture is
	pumped somewhere in the middle of
	the column and then the low
	boiling material goes up as a
	vapor to the top of the column
	and there it leaves the column
	and the other higher boiling
	material flows downward and
	leaves the column on the bottom.
	And to make it a bit more
	complicated, in our case the
	bottom stream is then pumped
	into a second column and
	distilled again, so to
	separate another material
	from the main..from...
	from the residual stuff. So
	actually we separated the
	original crude mixture into
three parts	Distillate 1,
	Distillate 2 and what's
	left, then the bottom stream
	of the second still.
	And to make it even more
	complex, we used the heat of the
	distillate of this second column
	to heat the first column in
	order to save energy for this.
	So in a schematic view,
	this looks like this.
	Here you have Still 1 and
	there's Still 2. And here we
	have the raw material mix which
	is actually assembly of
	distillates from the
	manufacturing and what we want
	to do is to separate the value
	material from the rest. So we
	pumped this crude mix into the
	first stills. As I said somewhere
	in the middle it separates in
	the low boiling point, which is the
	first value material we want to
	have, and the rest leaves the
	first still on the bottom. Then
	it's stored immediately here in
	a tank and then from here it's
	pumped again into the second
	still, again somewhere in the
	middle, and it separates into
	the second value material and
	away stream, which was then
	redeposited. To heat the stuff,
	we start on this side because
	this is the higher boiling
	material, so we need higher
	temperatures and this means also
	more energy. So we pump in the
	steam here, heat this still here,
	and the material leaving here on
	the top has the temperature of
	the boiling of this material, so
	we used the residual heat to
	heat the first still. And if
	there's a little gap between
	what's coming up from here and
	what we need for for
	distillation here in the first
	still, we can add a little
	extra steam to keep
	everything running.
	So this is a very high level
	view of the things, and if you
	want to go a bit more into the
	details, here it's kind of the
	same picture. But here I show
	you with all the different tags
	which we have here for all the
	readouts and quality control and
	temperature control and so on
	and so forth. So what you see
	here? We start here, for feed one
	into the first still.
	And then we separate into the top
	stream where we control the
	density, which is a quality
	characteristic. And in the
	bottom stream, the bottom stream
	goes in this intermediate tank.
	And then from here it's fitted
	again into the second still and
	also and separated here in the
	bottom stream for top stream.
	And again we are testing here
	density for quality control.
	Here also we add the steam to
	heat all these things up and
	the top flow then goes via heat
	exchanger into the first still
	and heat that up again. And here
	we have the possibility to add
	some extra steam to have
	everything in balance here.
	So what we see is on a local
	basis you have a lot of
	correlation, so this is done
	here with the color code of the
	arrows. So for example, the feed
	here together with the feed
	density, which is a measure for
	the composition here of this
	feed. So together with these two
	are defining the top stream and
	quality here, and that
	bottom stream more or less. So you
	have some local predictions. Also
	over here the material going in
	here and here defines stuff over
	here. But if you want to have a
	total description of the entire
	equipment, then it gets tricky
	because you can do local least square
	correlations here. You can do it
	here. You can do it separately
	for the steam or also here. But
	as you see, we have a start of
	the mass stream coming here, going
	through first still, through the
	second still, to here and we have
	an energy stream which starts
	more or less here, going through
	here via the heat exchanger also
	down here. So it's a kind of a
	circuit, which we have here, and
	all these things are correlating
	more or less in a kind
	of circuit and this gives gives
	us the difficulty that we
	actually didn't know what the Xs
	and the Ys were.
	And that was the reason where we
	started to think about other
	possibilities to model this.
	So the target for this study was
	to find the optimal flow and
	steam settings for all these
	varying incoming factors
	here, and in a way that we
	are able to stay everything
	in spec. So the distillate
	quality should stay in spec but
	also internal operational
	specs and also the spec for
	the final waste stream.
	And the most interesting part,
	at least money
	wise, we want to minimize
	the consumption of the
	speed...sorry, of the steam.
	So what we actually needed
	was first of all, a good model
	which describes this and that
	was the point where Laura came
	into the game here and developed
	this structural equation model.
	And we also need the kind of
	profiler which enables us to
	figure out what are the best
	settings, the optimal settings
	for all these incoming
	variations, which we may have
	here in order to stay within all
	these specs. And that was the
	point where Chris came in,
	building on the model from Laura,
	a profiler, which we can use for
	doing all the predictions we
	need. So now I want to pass
	over to Laura to describe the
	model she built from this
	data here. Laura, please.
	Thank you, Markus. I'm Laura
	Castro-Schilo and I'm going
	to tell you about the steps
	we followed to model the
	distillation process using
	the structural equation
	models platform.
	So when Markus first came and
	talked to us about his project,
	there were three specific
	features that made me realize
	that SEM would be a good tool
	for him. The first is that
	there was a very specific
	theory of how the processes
	affect each other, and we saw
	that on the diagram that he
	showed.
	An important feature of that
	diagram is that all variables
	had dual roles. In other words,
	you can see that arrows point at
	the nodes of the diagram, but
	those nodes also point at other
	variables, so there wasn't a
	clear distinction between what
	was an input and what was an
	output. Rather, variables had
	both of those roles.
	Lastly, it was important to
	realize that we were dealing
	with processes that were
	measured repeatedly. In other
	words, we had time series data
	and so all of these features
	made me realize that SEM would
	be a good tool for Markus. Now,
	if you're not familiar with SEM,
	might wonder why. SEM is a very
	general framework that affords
	lots of flexibility for dealing
	with these types of problems.
	I've listed in this slide a
	number of different features
	that make SEM a good tool, but
	since we're not going to be able
	to go through all of these, I
	also included a link where you
	can go with learn more about SEM
	if you're interested. Now I'm
	going to focus on two of the
	points I have here. The first
	is that SEM allows us to test
	theories of multivariate
	relations among variables,
	which was exactly what Markus
	wanted to do.
	Also, there are very useful
	tools in SEM called path
	diagrams. These diagrams are
	very intuitive and they
	represent the statistical models
	that we're fitting.
	So let's talk about that point a
	little more. Here is an example
	of a path diagram that we could
	draw in the SEM platform to
	represent a simple linear
	regression, and the diagram is
	drawn with very specific
	features. For example, we're
	using rectangles to represent
	the variables that we have
	measured. Here, it's X and Y. We
	also have a one-headed arrow to
	represent regression effects. And
	notice the double-headed arrows
	that start and end on the same
	variables represent variances.
	Now, if these were to start and
	end on a different variable,
	those double-headed arrows would
	then represent a covariance. In
	this case, we just have the
	variance of X and the residual
	variance of Y, which is the part
	that's not explained by the
	prediction of X.
	So this is the path diagram
	representation of a simple
	linear regression. But of course
	we could also look at the
	equations that are represented
	by that diagram. And notice that
	for Y, this equation is that of
	simply a linear regression. And
	I've omitted here the means and
	intercepts just for simplicity.
	It's important to note that
	all of the parameters in
	the equations are
	represented in the path
	diagram, so these diagrams
	really do convey the
	precise statistical model
	that we're fitting.
	Now in SEM, the diagrams or
	models that we specify imply a
	very specific covariance
	structure. This is the
	covariance structure that we
	would expect given the simple
	linear regression model. So you
	can see we have epsilon X as the
	variance of X. We also have the
	variance of Y, which is a
	function of both the variance of
	X and the residual variance of
	Y, and we also have an
	expression for the covariance of
	X and Y. And generally speaking, the
	way that model fit is assessed
	in SEM is by comparing the model
	implied covariance structure to
	the actual observed sample
	covariance of the data, and if
	these two are fairly close to
	each other, we would then say
	that the model fits very well.
	So a number of different models
	can be fit in SEM.
	And today our focus is
	going to be specifically
	on time series models.
	When we talk about time series,
	we're speaking specifically about
	a collection of data where there
	is dependence on previous data
	points, and these data are
	usually collected across equally
	spaced time intervals.
	And the way that time series
	analysis deals with the
	dependencies in the data is by
	regressing on the past. So one
	type of these models are called
	autoregressive processes or
	ARP. And you can see here, where
	Y represents a process that is
	measured at time T, the auto
	regressive models consist on
	regressing that process on
	previous observations of that
	process up to time T minus P.
	So if we're talking
	specifically about an
	autoregressive one process,
	then you can see we have the
	process YT regressed on its
	immediately adjacent past YT
	minus one.
	And the way that we would
	implement this in SEM is simply
	by specifying, as we saw before,
	the regression of YT on YT minus
	one. So notice that here the
	regression equation is very
	similar to what we saw in
	the previous slide, and so
	it's no surprise that the
	path diagram looks the same.
	And we can extend this AR(1)
	model to one that includes two
	lags, in other words, an
	autoregressive of order two. And
	here we see we have the process
	YT that is being regressed on
	both T minus 1 and T minus 2.
	And if we look at the path
	diagram that represents that
	model, we see that we have an
	explicit representation for the
	process at the current time, but
	also at the lag one and lag two.
	A very specific aspect of this
	diagram is that the paths for
	adjacent time points are set to
	be equal to each other, and this
	is an important part of the
	specification that allows us to
	specify the model correctly. So
	notice here we're using beta 1
	to represent this lag 1
	effects and we also have to
	set equality constraints on
	the residual variances.
	Lastly, we also have the effect
	of YT minus 2 as it's
	predicting YT, and so here's the
	lag 2 effects.
	Now all of these models are
	univariate time series models,
	and you can fit them using the
	structural equation modeling
	platform in JMP or you could
	also use the time series
	platform that we have available.
	However, the problem we were
	dealing with with Markus' data
	require more complexity. It
	required us to look at
	multivariate time series models
	and a type of these models are
	called vector autoregressive
	models. And what I'd like to
	show you is one of these models
	of order two.
	So we have a process for X and
	another one for Y, and the same
	autoregressive effects that we
	saw before are included here.
	Notice we have our equality
	constraints which are really
	important for proper
	specification. But we also have
	the cross lagged effects which
	tell us how the processes
	influence each other. And notice
	here gamma 1 and gamma 1 and
	also gamma 3, gamma 3,
	suggesting here that we have to
	put equality constraints on
	those lag 1 effects across
	processes.
	We also have to incorporate the
	covariances across the processes
	so we have their covariance at
	time T minus 2. But we also have
	the residual covariances at time
	T minus 1 and T and notice these
	have to have equality
	constraints again to have proper
	specification. So I'm going
	to show you in this video how
	we would fit a bivariate time
	series model just like the
	one I showed you, using JMP
	Pro. We're going to start by
	manipulating our data so that
	they're ready for SEM. First,
	we standardize these two
	processes because they are in
	very different scales.
	Then we create lagged variables
	to represent explicitly the time
	points prior to time T. So we're
	going to have
	T minus 1 and T minus 2.
	We launched the SEM platform and
	we're going to input the Xs
	then the Ys so that it's
	easier to specify our models.
	And now I sped up the video
	so that you can quickly see
	how the model is specified.
	Here we're adding the
	cross lagged effects
	for lag 1.
	And then directly using the
	interactivity of the diagram, we
	add the lag 2 effects.
	And what remains is to
	specify all the equality
	constraints that are required
	for these models within
	process and across processes.
	We name our model.
	And lastly, we're
	going to run it.
	As you could see, even just a
	bivariate time series model that
	only incorporates two processes
	requires a number of equality
	constraints and nuances in the
	specification that make it
	relatively challenging. However,
	in the case of the distillation
	process data, we had a lot more
	than two processes. We were
	actually dealing with 26 of
	these processes and in total we
	had about 45,000
	measurements, which were taken
	at 10 minute intervals.
	And so our first approach was
	to explore the univariate
	time series models using the
	time series platform in JMP.
	And when we did this, we
	realized that for most
	processes an AR(1) or AR(2)
	model fit best, and so this
	made me realize that really
	at the very least we needed
	to fit multivariate models in
	SEM that incorporated at
	least two lags.
	We also had to follow a number
	of preprocessing steps for
	getting the data ready into SEM.
	On the one hand, we had a lot of
	missing data, and even though
	SEM can handle missing data just
	fine, with models that are as
	complex as these ones, it became
	computationally very very
	intensive. And so we decided to
	select a subset of data where we
	had complete data for all of the
	processes and that left us with
	about 13,000 observations.
	Also, as we saw in the video, we
	had large scale differences
	across the processes, so we had
	to standardize all of them. And
	lastly we created lag variables
	to make sure that we could
	specify the models in SEM.
	Now for model specification,
	equality constraints in
	particular are very very big
	challenge because it would take
	a lot of time to specify them
	manually and it would be, of
	course, tedious and error-prone.
	So our approach for dealing with
	this was to generate a JSL
	script that would then generate
	another JSL script for launching
	the SEM platform.
	And what you see here is the
	final model that we fit in the
	platform and thankfully, after
	estimating this model, we are
	able to obtain a covariance
	structure that is implied by the
	model and that was the piece of
	information that I could pass
	over to Chris Gotwalt, who
	then used the information from
	that matrix in order to create a
	profiler that Markus could use
	for his purposes.
	So Chris, why don't you tell us
	how you created that profiler?
	Thank you, Laura. Now I'm going
	to show the highlights of how
	I was able to take the model
	results and turn them into a
	profiler that the company
	could easily work with.
	So Laura ran her model on the
	standardized data and sent me a
	table containing the same model
	intercepts and she also included
	the original means and standard
	deviations that were used to
	standardize the data. On the
	right we have the sim model
	implied covariance matrix, which
	includes the covariances between
	the present values and the
	lagged values from the immediate
	past. This information describes
	how all the variables relate to
	one another. In this form,
	though the model is not ready to
	be used for prediction. To see
	how certain variables change
	as a function of others, we have
	to use this information to
	derive the conditional
	distribution of the response
	variables, given the variables
	that we want to use as inputs.
	So essentially we need the
	conditional mean of the
	responses given the inputs. So
	to do that, we need to implement
	this formula right here.
	And to do that, we use the SWEEP
	Operator in JSL, the SWEEP
	Operator is a mathematical tool
	that was created by SAS CEO and
	co-founder Jim Goodnight. It's
	was published in the American
	Statistician in 1979. The SWEEP
	Operator is probably the single
	most important contribution to
	computational statistics in the
	last 100 years. Most JMP users
	don't know that the SWEEP
	Operator is used by every single
	JMP statistical platform in
	many ways. We use it for matrix
	inversion, the calculations that
	sums of squares and also can be
	exploited as simple and elegant
	way to compute conditional
	distributions if you know
	how to use it properly.
	I created a table with columns
	for all the variables. The two
	rows in the table are the
	minimums and maximums of the
	original data, which lets the
	profiler know how to set up the
	ranges. I added formula columns
	for the response variables using
	the swept version of the
	variance matrix from Laura's
	model and put those formulas
	into the back here in the data
	table or the far right.
	Here's what one of the formulas
	looked like. I pulled in the
	results from the analysis as
	matrices. Laura's model included
	the estimated covariance between
	the current Ys in the last two
	preceding values because it was
	a large multivariate
	autoregressive model of order 2.
	Predicting the present by
	controlling the two previous
	values of the input variables
	was going to be very cumbersome
	to operationalize. So I made
	a simplifying assumption that
	these two values were to be
	the same, which collapsed the
	model into a form that was
	easier to use. To do this, I
	simply use the same column
	label when I was addressing
	into the lag one, and
	lag two entries for term.
	Without machinery in place,
	I created a profiler for the
	response columns of
	interest. I set up
	desirability functions that
	reflected the company's
	goals. So they wanted to
	match a target on on
	A2TopTemp, maximize A2BotTemp,
	and so on, ultimately
	wanting to minimize the sum
	of the steam that came out
	of the two columns.
	So you can lock certain
	variables in the profiler by
	control clicking on a pain. The
	lock variables will have their
	value drawn via a solid red
	line, and then once we've done
	that we can enter values for
	them, and when we run the
	optimizer or maximize
	desirability, the locked
	variables will be held fixed.
	This way we find settings of the
	variables that we can control
	that keep the product being made
	to specification while
	minimizing energy costs.
	It's fair to say that it would
	be difficult for someone else to
	repeat Laura's modeling approach
	on a new problem, and it would
	be difficult for another person
	to set up a profiler like I did
	here. If enough people see this
	presentation and want us to add a
	platform that makes this kind of
	analysis easier in the future,
	you should let us know by
	reaching out to Technical
	Support via support@JMP.com.
	Now I'm going to hand it back
	over to Markus who will talk
	about what the customer did with
	the model and our conclusions.
	Thank you, Chris.
	So with the prediction Profiler,
	which Chris just presented, we
	used that to, let's say, make a
	predictive landscape, which
	makes us understanding how
	the best settings should be in
	order to achieve all the
	necessary quality specs. And so
	what the three factors which
	are, let's say, are varying with
	limited extent to our influence,
	and what's the
	feed for the Still 1 and the
	feed for the Still 2 and also
	the quality or the composition
	of the feed into one.
	And what we've turned out as in
	their model as well, is the
	cooling water temperature was
	also playing an important role
	in this scenario. All the other
	variables are of smaller importance
	so that we neglected them in
	this first approach.
	Here you see the landscape.
	It's kind of a variability
	chart, so to say, so we have
	here the feed density for the
	Still 1, the feed into Still 1
	and the feed into Still 2
	and all possible combinations,
	more or less. And here you see then
	the settings which are predicted
	to be best in order to stay
	within the specifications. And
	here are some of these I have
	specifications as well, so we
	have to stay inside them.
	So it's, for example, it's the
	steam flow for Still 2, the
	reflux there, the boiler up and
	the same things for the
	Still 1. And here on the right
	side, you see the
	predicted outcomes, so the
	quality specs, so to say. So the
	temperature of the top flow in
	Still 1 that the density
	of the distillate, the density
	of the distillate of Still 2,
	and so on and so forth. So what
	you see is here, if you have a
	look here on the desirability,
	which is the bottom row here,
	there's big areas where
	we cannot really achieve a good
	performance of our system. And if
	you have a look into the details
	you see, OK, here we are off spec,
	here we are off spec, here on
	some points, we are off spec, and so on
	and so forth. But what else it sees
	is that this in spec/off spec thing
	is also governed not only by
	these three components down
	here, but also by the river
	temperature, and for the moment
	it's highlighted the lowest
	river temperature; it's 1
	degree. So this you see here
	with it, we are staying most of
	the time in specs, though there
	only are rare
	combinations of these three
	factors where we aren't. But if
	we are increasing the river
	temperature, for example for 24
	degrees, then the areas where we
	are off spec are...become much
	more predominant. Also here it's
	very hard to stay within this
	specifications. So what we
	learned from the model
	is that we have problems to stay
	in our specifications when the
	river temperature is above 7
	degrees C. So then then the
	the question was why is
	that? And the engineers
	often...suspected is that
	this was because of the
	cooling capacity of the
	coolant. But before we went
	into the real trial, we
	compared our SEM model versus a
	thermodynamic model based
	on Chem CAD.
	And what it pointed out was
	that both models are
	pointing in the same
	direction, so there were no
	no real discrepancies
	between the both.
	OK, this made us in an
	optimistic mood and so we did
	some real trials
	and with the best
	settings, and let's say,
	approved the the predicted
	things from the models.
	And so it turned out, as I said
	already, that what the engineer
	suspected that the cooling
	capacity of the cooler is not
	sufficient. And so when you have
	at higher river temperature,
	then the heat transfer is too
	small, and so the equipment...
	equipment doesn't really run
	anymore. So the next step now is
	to use these data from from this
	study here to justify another
	investment which builds
	a cooler here with a better
	heat exchange capacity.
	So thanks to Laura and Chris, we
	could set up here the investment.
	If you have questions so
	please feel free to ask now.
	Thank you.