Profit Maximization Techniques for Operating Chemical Plants. Sandip K. Lahiri
Читать онлайн книгу.
Sales and Marketing
Data analytics and AI‐based digital technology can be used for intelligent decision making in sales and marketing. Mckinsey estimate‐ that digital‐enabled initiatives in marketing and sales could improve the industry's average return on sales (ROS) by two to four percentage points.
Digital initiatives in marketing and sales include developing intelligent pricing systems, generating growth opportunities from data, and using algorithms to predict churn at the individual‐customer level and then suggesting countermeasures to the sales force. The impact of these initiatives can be significant. A large polymer company used advanced analytics to reset prices for hundreds of thousands of product‐customer combinations in three core countries, based on individual risk and willingness to pay. By developing an AI‐based intelligent algorithm, the company was able to achieve price increases of 3 to 5%, compared to 1% increases in previous years. In some other petrochemical companies, the company's manufacturing unit is connected with the sale and marketing unit by an optimization algorithm and the company's production plant process parameters, product split in a multi‐product plant, and capacity are adjusted by the demand scenario coming from sales and marketing forecasting.
2.3.1.4 Research and Development
Due to plastic pollution, pollution from cars and from various carcinogenic chemicals, the usage patterns and demands of various chemicals across the globe is changing very fast. This poses a challenge to chemical industries who makes those products.
One of the ways a research and development department of chemical plants can respond to this challenge is by creating higher‐value‐added, higher‐margin products at a faster pace, in particular in specialty chemicals and crop‐protection chemicals (Klei et al., 2017). Through intelligent algorithms, chemical companies will be able to use high‐throughput optimization to develop and adjust molecules that offer more value. They will also be able to deploy advanced analytics and machine learning to simulate experiments, to use digital predictive power to systematically optimize formulations for performance and costs, and to data‐mine information available from past successful and failed experiments. Not least, they will be able to identify the best possible resource allocation to enhance the performance of R&D teams and the innovation pipeline. Many of these practices are already established in the pharmaceutical industry but were largely unaffordable for chemical companies. With the emergence of inexpensive computing power on a massive scale, this is likely to change.
2.4 Using Advanced Analytics to Boost Productivity and Profitability in Chemical Manufacturing
As of now, it is quite clear that digital will have a significant impact on many areas of the chemical industry, with the gains in manufacturing performance potentially among the largest companies (Holger Hürtgen, 2018). Chemical companies have already created the infrastructure to collect and store enormous amounts of process data from hundreds of thousands of sensors, but very few have succeeded so far to take advantage of this data gold mine of potential intelligence. With the availability of cheaper computational power, IoT‐based cheap sensors, and intelligent advanced analytics tools, all chemical companies can now use those data to make more profit, extract knowledge from those data, and using machine‐learning and visualization platforms to uncover ways to optimize plant operations (Wang, 1999).
AI‐based machine learning tools can be used to develop insights into what happens in a chemical plant's complex manufacturing operations; this can help chemical companies solve previously impenetrable problems and reveal those that they never knew existed, such as hidden bottlenecks or unprofitable production lines.
There are three major areas where applications of advanced analytics tools can give an enormous profit increase, namely predictive maintenance; yield, energy, and throughput analytics; and value‐maximization modeling, as shown in Figure 2.3 (Wang, 1999).
Figure 2.3 Three major impact areas where advance analytic tools will help to increase profit
2.4.1 Decreasing Downtime Through Analytics
One of the major profit suckers in chemical industries is a sudden trip of critical single line equipment. Once a plant trips, millions of dollars get lost in terms of less or no production and more time is required to bring back the plant to on‐spec production after a disturbance. Besides this, a lot of money is lost in terms of flaring, venting, or draining of costly chemical gas or liquids (Wang, 1999).
Big data analytics can be used to develop fault diagnosis software to anticipate the failure of critical equipment at a very early stage and thus give sufficient time to plant engineers to take preventive or corrective actions. Such fault diagnosis systems analyze historical data to generate insights that cannot be observed using conventional techniques. By implementing an intelligent analytics‐based fault diagnosis system, companies can determine the circumstances that tend to cause a machine to break. Then a real‐time automated system can be developed to monitor all relevant parameters and give early fault signals, so engineers can intervene before breakage happens, or be ready to replace a component when it does, and thus minimize downtime. Companies who has implemented such systems typically reduce machine downtime by 30 to 50% and increases machine life by 20 to 40% (Wang, 1999).
Chemical companies are already starting to see substantial gains in this area. One major polymer producer consistently ran into problems with extruders at its largest plant. When one of the shafts of the extruder broke, the plant had to stop production for 3 days while a replacement was installed; these shafts are expensive, besides the cost impact of the production loss. Engineers had done a detailed study to determine the possible root causes of failure; alternative materials in the shaft were also tried out, as well as different process conditions, but none of them solved the problem.
A principal component‐based fault diagnosis approach changed all this. It combined a detailed analysis of data from hundreds of sensors with the plant engineers' expert domain knowledge, and reexamined the process variables and other data sources; it then developed a real‐time‐based algorithm to predict when a failure was imminent. The problem occurred with only one of the polymer grades, and not with all batches, suggesting the key lay in specific process conditions in the equipment. The team developed a model based on “a hybrid principal component analysis and artificial neural network” algorithm that took into account the specific parameter settings in production, such as extremes of temperature and temperature progression, together with information on the polymer product type and composition.
A real‐time visual platform was developed, which flagged an early warning to engineers when the plant conditions approached a state that could ultimately lead to shaft failure.
When it flags that a failure is imminent, the plant operators undertake a 15‐minute cleaning of specific parts of the machinery to prevent the failure from occurring. The improvements to performance that resulted from using the advanced‐analytics approach have been substantial. Instead of a 3‐day production loss plus a costly extruder shaft replacement, the company was now dealing with just a 15‐minute production interruption, and the approach has cut production losses by 60% and maintenance costs by 85%.
2.4.2 Increase Profits with Less Resources
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