Читать книгу Profit Maximization Techniques for Operating Chemical Plants - Sandip K. Lahiri - Страница 48

Whereas predictive maintenance and yield, energy, and throughput analyses are designed to improve the efficiency and profit‐making capability of individual pieces of equipment, value‐maximization modeling covers the whole plant or whole site and helps to optimize the interaction between those pieces of equipment across processes. This optimization and modeling technique utilizes its inherent analytic capability to show in real time how to maximize the rate of profit generation in complex production systems and supply chains, encompassing every step from purchasing to production to sales. Unlike the limitations of human planners, this advanced‐analytics approach typically solves the complex maze comprising as many as 10 000 variables and one million constraints to help producers figure out what to buy and when, what to make and how much, and how they should make it to generate maximum profit in each period (Wang, 1999).

The uncertainty in pricing and demands of turbulent chemical markets poses a complex business challenge, which needs to be solved every day to figure out the most optimum buy and sell decision and also how much to produce. The uncertain and frequently changing nature of chemical companies' businesses and product lines means they must be capable of solving a complex objective function: volatile costs and prices, multiple plants, and products that can be made in various ways from diverse combinations of materials, involving output of different combinations of co‐product of varying values, maximizing output of a high‐value product, as well as managing by‐product flows.

The following example from one large, diversified integrated refinery and petrochemical complex shows the kinds of gains to be captured. The company was selling a broad range of petrochemicals and specialty chemicals from the site to a global marketplace through a mixture of spot and long‐term contracts. On the other hand, it was buying the raw material, i.e. crude oil, from various countries with varying quality and price.

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%.