ChemE.info – Chemical Engineering Consulting

Ammonia plants use a combination of steam reforming and shift reactors to convert air, steam, and a hydrocarbon feedstock to a stream containing hydrogen, nitrogen, and CO2. The CO2 needs to be removed before the H2/N2 stream is sent to the synthesis loop. The CO2 removal is accomplished using a caustic scrubbing system.

I was developing an overall ammonia plant model for a customer in Japan. About mid-way through the project my Japanese colleagues came to the US for a review meeting in which we tried to match the model results against plant operational data (temperatures, pressures, flow meter readings, chemical analyses).

Everything was matching up rather well… Except for the chemical analysis of the CO2 scrubber underflow. The plant’s analyses were consistently much lower in dissolved CO2 than what my model was calculating. We went round and round trying to figure out what might be wrong with the model physical properties, the control settings for the absorption and stripping columns, etc. We just couldn’t see anything wrong with the model. We could force it to match the plant’s underflow analysis but we then had much too much CO2 getting into the synthesis loop. So my colleagues called the plant back in Japan and asked if there was any way their analysis could be incorrect. They were told (rather vehemently) that there was no way that the analyses could be in error… That plant personnel had taken the underflow sample every shift for nearly twenty years and that the analysis was always the same.

That’s where the light began to dawn. My Japanese colleagues and I had naively assumed that the analysis was the output from some inline automatic analyzer. When I was told that plant personnel “took the sample” I began to suspect that this was a case of sampling error. The underflow from the Benfield unit would be rather hot and at some pressure. I asked my colleagues to call back and ask how the sample was taken and whether it was kept at the same pressure until it was analyzed.

While they checked back with the plant, I reran the model including an underflow sample stream that I flashed to atmospheric pressure. And the model’s predicted sample composition matched the plant analysis perfectly. And then my colleagues got off the phone and confirmed that the sample was taken by opening a valve and catching a stream of hot caustic in a bucket. So the problem was solved. When the pressurized caustic solution was dropped to atmospheric pressure a large part of the CO2 flashed off and that totally changed the sample composition. And the analysis was always the same because it was always flashed to the same state.

We also discussed the point that this sampling exercise was both dangerous to the operator and totally pointless. I later found out that the plant discontinued the practice as soon as my colleagues got back to Japan.

Model validation is a critical step that must be performed before a model can be safely used to study or improve a process. But validation is very much an art. Most large scale process models assume steady-state operation but no large process is every really at steady-state so trying to decide when you are “close enough” is a challenge. In addition, the operational data that you must validate against always has errors. In my experience over modeling dozens of plants, it works out about 50/50. In other words, if you have a significant discrepancy between the model and the plant data about half the time you’ve done something wrong in the model and about half the time the plant data is wrong. That ratio obviously depends on how careful you are in your initial model development and how well run the plant is.

Australia is a large country (with a small population) which has lots of coal reserves, quite a lot of natural gas, and virtually no oil.

Back in the 1980’s after the first Middle East oil embargo and against the continuing backdrop of Middle East political unrest, the Australian Department of Primary Industries decided to study whether and how Australia’s abundant coal reserves might be used to produce synthetic liquid fuels (e.g. diesel, jet fuel, gasoline, etc.) in case imported oil became scarce or unavailable.

DPIE (never, we were told, to be pronounced “dopie” ;) ) commissioned Broken Hill Proprietary’s R&D arm to develop a pilot-scale coal liquefaction process and run experiments on the various coals available in Australia. My recollection is that they looked at coals from the Victoria, New South Wales, and Queensland.

For those unfamiliar with Australia, Broken Hill Proprietary (or BHP) is the 800 lb gorilla of the Australian economy. Not only is it a huge company but is also the primary player in terms of industrial R & D. In a US context, it is as if you combined IBM, GM, GE, and Microsoft into a single entity.

So BHP set up pilot plant facilities at their R&D campus in a suburb of Melbourne and began running tests on the various domestic coals. The actual liquefaction process was largely based on what had already been shown to work in the US and Europe.

I don’t remember the BHP process in any detail but, like all coal liquefaction processes, it involved processing pulverized coal with coal-derived liquids and hydrogen under high pressure and high temperature. This then produced two streams, an ash residue stream and a liquid roughly comparable to crude oil. It was this synthetic crude oil that was intended for additional processing to produce synthetic diesel, kerosene, and gasoline. A fraction would also be recycled back to liquefy additional coal.

Of course, the pilot plant process was intended to collect data on the process and the immediate synthetic crude product. It did not provide any directly useful information on the overall process economics.

So DPIE commissioned us (AspenTech) to develop a simulation of the complete process including coal pre-processing, coal hydroliquefaction (based on the BHP pilot-plant data), the synfuel refining section, and all the other support sections (e.g. hydrogen production).

The simulation was intended to represent an actual commercial-scale plant, its operating costs, and capital costs with a view to determining what the net cost of the final transport fuels would be in equivalent dollars per barrel. This would then give one idea of how high world oil prices would have to be for a coal-based synfuel plant to be competitive.

The other purpose of the modeling effort was to ensure that BHP was collecting enough consistent data to support such a study.

The process side simulation was challenging (this was a large model with a lot of distillation columns, reactors, and recycle streams) and the economic side required a lot of assumptions. For example, databases used to estimate capital equipment costs were US-based, no Australian capital equipment cost data was available.

It was a very interesting, challenging project and I enjoyed my stay in Australia (I was out there for a total of 6 months) and it was fun working with my colleagues at BHP.

The conclusion of the project was rather bemusing and, I suppose, shows how naive engineers are. One of the things we’d been asked for in the RFP was a comparison of the process economics for the different Aussie coals (Victoria, New South Wales, and Queensland). So our final report had a table comparing the results and we had text discussing this… Basically, the model showed that Queensland coal had the best economics and our conclusions said as much. But DPIE kept delaying approval of the report and, since our final payment was dependent on the report being accepted, we were getting a bit anxious. But no one was giving us any specifics on why the report was not being accepted.

Eventually, one of the BHP managers had to give us a little explanation of Australian politics… That Victoria was a much more populous state than Queensland and therefore had more MPs and more clout in the Federal bureaucracy than Queensland did… And that DPIE did not want to approve our report while it explicitly stated that the Queensland coal was a better choice than the Victoria coal. (Neither were they willing to tell us that directly.. ;) )

So we changed the text of the conclusions… The comparison tables still showed that the Queensland coal produced less expensive synthetic fuels but we didn’t explicitly mention that in the final conclusion. And… The revised report was accepted.

Now that world oil prices are up around $60 a barrel, I wonder if anyone in Australia is revisiting this area to see what the current synfuel economics look like.