Читать книгу Profit Maximization Techniques for Operating Chemical Plants - Sandip K. Lahiri - Страница 47

Increasing plant throughput is the best and most effective way to increase plant profit. In plant, every equipment has some extra margin rising from its design safety margin. Advance analytics can scrutinize the past 3–4 years of plant operation data and can estimate how much capacity increase is possible in the plant without investing a single penny. AI‐based optimization techniques can then be used to optimize process parameters so that these safety margins can be exploited to increase plant yield and throughput or minimize energy costs. Even small percentage improvements in operational efficiency can significantly enhance earnings before interest, taxes, depreciation, and amortization (EBITDA) performance. The approach does that by balancing yield, energy use, and throughput – while also taking account of varying raw materials costs – to maximize the profitability of each process step (Wang, 1999).

One petrochemical company was having capacity limitations at its naphtha cracker furnace that makes ethylene and propylene. The furnace's unstable production rate and low overall output meant that it represented a serious bottleneck for a high‐margin segment of output. An artificial neural network and genetic programing‐based furnace model building exercise was carried out. All the temperature, pressure, flow, and composition data related to the furnace had been collected over 3 years of production, comprising 900 000 samples, each with 360 tags – almost 80 million data points. This analysis identified critical process parameters and made it possible to build a data‐driven furnace model. The model generated mathematical relations between the furnace throughput and other input parameters that can influence the throughput. By running the models at different input conditions, a deeper understanding of furnace operations is generated. A test run of the furnace confirmed the model's findings. The plant engineer had long suspected that manipulating some of the levers identified in the model could improve productivity, but they never had the mathematical tools or data to confirm it. Based on its new advanced analytics‐based understanding of its process, the company developed an automated, real‐time‐based operator guidance platform that advises the operators how to adjust a range of process parameters to get the best performance. The result was an output increase of 10 to 15%, which represented a net profit‐contribution increase of around USD 15 million a year. The company estimates that applying the same kind of advanced‐analytics approach across all the different manufacturing operations at the site could generate a USD 75 million annual profit gain (Holger Hürtgen, 2018).

Advance analytics can also be used to develop a data analytic platform to show a companywide energy usage pattern and guide the operators to change various energy usage options to minimize energy consumption. AI‐based techniques are sometimes use for a simple process, such as how to exploit favorable seasonal conditions (say a less cooling water temperature) to improve plant profitability.