Profit Maximization Techniques for Operating Chemical Plants. Sandip K. Lahiri

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Being a multinational company with a presence in different countries, purchase, sales, and production decisions were made by local offices and pricing was arbitrarily set by different regions and departments. Organizational responsibilities were scattered across multiple business units and corporate functions. Underlying all this was the typical chemical‐industry challenge of commodity products underpinning specialties production, while the commodity output brought with it lower‐value co‐products, multiplying the hurdles to maximizing profitability. Due to the absence of a global optimization algorithm, the company lost a lot of money due to non‐optimal decisions that were taken locally. A mixed‐integer programming model encompassing the 900 variables explored nonlinear cost curves and the 4000 constraints related to production capacities, transportation, and contracts; the hundreds of steps in production with alternative routes and feedback loops; nonlinear price curves and raw‐materials cost structures; and intermediate inventories (Wang, 1999).

      Using the model, the team solved a global optimization problem and were able to increase profits by USD 20 million a year (Wang, 1999). For example, the company started making an intermediate product on an underused line instead of buying it from a third party. At the same time, the team optimized different process parameters of a furnace, various distillation columns, an absorber, etc., which gave higher yields, thereby reducing raw‐material consumption. It identified some extra cushions available in some of its plant to expand capacity by increasing the throughput, and it increased sales revenues by raising the capacity for some product categories. It also maximized the production of some of the products that fetched a higher profit margin.

      The analytics approach revealed some counterintuitive improvements. The model suggested that eliminating the production of a particular polymer grade would increase profitability overall. The company had been selling this lower‐grade polymer to a local customer for a long time, but generated limited returns while incurring high logistical costs. By shifting the raw material, i.e. ethylene, used to make this polymer, to manufacturing another value‐added product, the company was able to make more profit. That switch might never have been suggested if the decision had been left to the manager of the polymer business, who previously had the decision rights.

      These changes enabled the chemical company to boost its earnings before interest and taxes by more than 50%.

2.5 Achieving Business Impact with Data

      For the last two decades chemical industries have been generating, collecting, and storing huge amounts of operation and maintenance data using various software. These data are like a gold mine and now is the best time to achieve an impact with (your) data. More and more data are available, computing power is ever increasing, and mathematical techniques and the so‐called data science are becoming more and more advanced. Yet while data is considered as the “new oil” or the “new gold” these days, several technology‐ and business‐related challenges prevent chemical industries from realizing the potential impact data can make on their business (Holger Hürtgen, 2018).

      2.5.1 Data's Exponential Growing Importance in Value Creation

      The following facts regarding data have changed the business outlook in recent times:

       Rapid increase in data volume: The number of delivered sensors globally has increased sevenfold from 4 billion in 2012 to greater than 30 billion in 2016 (Mckinsey). Data has not only increased exponentially in volume but has also gained tremendous richness and diversity. In the chemical industry, data is not only generated from various flow, temperature, and pressure transmitters but also from cameras and analyzer to vibration monitors, enabling richer insights into process behavior.

       Falling IoT sensor price: There was a 50% reduction in IoT sensor price between 2015 and 2020.

       Cheap computational power: Better processors and graphics processing units increased investment in massive computing clusters, often accessed as cloud services, improvements in storage memory, etc., have recently increased computational power.

       New data analytic tools: In recent times, many new tools have been coming to market to convert this flood of raw data into insights and eventually into profit.

       Machine learning and artificial intelligence: These new generation algorithms are rapidly replacing the old method of calculations and emerge as new data analytics. Both data and computational power enable next‐generation machine learning methods, such as a deep learning neural network.

       Value creation: As a consequence, data has become the new oil of the chemical industry – and the best way for companies to generate and access is to digitize everything they do. Digitizing customer feedbacks provides a wealth of information for marketing, sales, and product development, while digitizing manufacturing processes generates data that can be used to optimize operations and improve productivity.

      The confluence of data, storage, algorithms, and computational power today has set the stage for a wave of creative disruption in the chemical industry.

      2.5.2 Different Links in the Value Chain

Data in its raw and most basic form is virtually worthless until we generate knowledge and business insights from it. The biggest challenge to confront the chemical industry today is how to generate business insights from these huge data banks sitting in their server and convert that knowledge to increase profit. Today every leading chemical industry talks about Big Data and Advanced Analytics and even machine learning and artificial intelligence (AI). Today's leading chemical industry is in a hurry to implement the advance analytics in their business and they focus too much on single technical components of the “insights value chain,” as we call it. However, the value creation of data consists of following five components and companies need to focus on all the components if they want to capture the full value (or any value at all) from relevant (smart) data (Figure 2.4):

(2.1)

It is important to understand Equation (2.1), which reveals that the insights value chain is multiplicative, meaning that if one single link in that chain is zero, your impact will be zero. In other words: the entire data ecosystem is only as good as its weakest component. The chemical industry needs to understand this critical concept and should give importance to developing all components and steps of the insights value chain – not focusing on only one piece and forgetting about the others.

The following sections briefly explain the function of each of the insights value chain's core components (see Figure 2.5) along with its upstream as well as its downstream steps and processes.

Figure 2.4 Different components of the insights value chain

Figure 2.5 Overview of the insights value chain upstream processes (A–B) and downstream activities (D–E)

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