Читать книгу Profit Maximization Techniques for Operating Chemical Plants - Sandip K. Lahiri - Страница 29

There has been a drastic change of business environment in the last 20 years. Shrinking profit margins in chemical process industries (CPIs) due to globalization and an uneven level playing field in international chemical businesses have given rise to cut‐throat competition among process industries and has changed the global chemical business scenario (Lahiri, 2017b). Introduction of low cost technology in the market, cheap oil prices, a decline in the growth rate of Chinese and EU economies, the recent discovery of cheap US shale gas, for example, have added new dimensions in the business environment in recent years.

Modern process industries are experiencing the following new challenges in their businesses:

 Enforcement of stringent pollution control laws.

 Pressure from government agencies to change to more energy efficient processes and equipment.

 Constant encouragement from government to shift to safer and less pollutant processes and technology.

 Decline of sales prices of end products in the international market.

 More focus on sustainability and more reliable processes.

All these issues are forcing the process industries to look for new technological innovations so that new ways of doing business can be explored.

Profit maximization is at the core of every chemical company's vision and mission nowadays. Making money by safely producing chemicals and selling locally are no longer adequate to survive in today's business environment.

Maximization of profit, continuous improvement of operation, sustainability, and enhanced reliability to reduce production cost are buzzwords in today's CPI. Industries are slowly shifting their priorities to energy efficiency, real‐time process optimization, environment friendliness, and sustainability.

Running the plant at their highest feasible capacity by exploiting the margin available in process equipment is no longer a luxury but a necessity. Maximizing the profit margin by reducing waste products, by increasing mass transfer and energy efficiency of equipment, and by pushing the process to their physical limit are the current trends of CPI (Lahiri, 2017b).

Another challenge process industries are facing is the unprecedented fast rate of obsolescence. Chemical process technology and equipment are becoming obsolete at a very fast rate. New generation energy efficient processes, environment friendly low waste generated processes, and higher selectivity catalysts are coming to market every year and pose a survival threat to older plant and technology. Simple distillation columns that have dominated the process industries and refineries for the last few decades are no longer remaining competitive. Reactive distillation columns and low energy intensive membrane technology are slowly replacing the older technology. Apart from profit maximization, business objectives also venture out to the fields of sustainability, reliability, low environment impact, etc. With the advent of faster computers, online optimization, advance process control, real‐time plant monitoring, online equipment fault diagnosis, big data‐based process parameter data analytics, artificial intelligence (AI), and internet of things (IOT)‐based sensors and technology have found their way into process industries.

During this turbulent time, people and industries that cannot change their way of doing business quickly gradually become obsolete. Many industries and technologies that were running 25 years ago no longer exist as they could not cope with the changing requirements of modern times. After globalization, chemical industries needed to purchase their raw material and sell their finished products to the international market at the international market price. They have to compete with cheap raw material prices of OPEC countries, cheap manpower costs of China, big capacity of the Middle East plants, etc. Most of the traditional industries could not cope with this uneven competition and gradually became obsolete. Hence, to survive in today's business environment, industry needs to find new ways of doing business. Therefore, a completely new way of thinking and optimization is required to maximize profit. A complete paradigm shift in mindset is the need of the hour.

Profit maximization is one of the prime goals of every management team of process industries across the globe.

Profit maximization in running chemical plants is itself a huge challenge that needs to be addressed by holistic vision and procedures. What is the objective of profit maximization in the process industries? It is not simply waste or loss minimization and it is not simply energy and raw material minimization. It is much bigger than that. The ultimate goal of profit maximization is to use every resource available to the process industry in the most efficient manner so that dollar per hour generation is maximized and sustained over years. To achieve this goal, energy, raw materials, equipment, manpower, processes, and environment as well as the mindset of the people must be aligned and be holistically optimized.

Most of the time, engineers and managers working in process industries tackle this problem with a fragmented approach. Process engineers try to optimize process parameters, production engineers try to meet a production target rate, energy managers want to minimize energy consumption, maintenance engineers try to increase equipment uptime, reliability engineers try to increase equipment and process an on‐stream factor, safety engineers try to reduce accidents and incidents, etc. All of them try to increase profit in their own way without paying attention to what other people are doing. Sometimes, their objectives are contradictory. However, a profit maximization project needs to holistically combine all their effort in the most efficient way so that sustainability and profit maximization are achieved.

The main constraints of a technical work force in various process industries today is that they are too loaded with various types of managerial and technical jobs that they do not have time to see things holistically. Furthermore, the mind sets of plant managers and engineers are not aligned with the larger vision of profit maximization and sustainability. They are not equipped with techniques and methods that can combine all the fragmented approaches and do an overall optimization. No such procedures have been available until now. Therefore, the current challenge of a profit maximization project is: how can we develop a road map so that all of the available intelligent tools and techniques can be utilized effectively to support plant engineers as well as top management?

Over the years, engineers working in process industries have believed that reducing loss and waste is the only way to achieve efficiency. Thus, to them, energy efficiency means to identify and rectify steam leaks, faulty steam traps, damage insulation in steam lines, less flaring, cleaning of fouled exchangers, etc. In the last decade they focused on these basic old housekeeping parts of energy efficiency. These are the techniques of past decades. With the advent of faster computers and data historians, every minute many process parameter data are stored, with the availability of offline process simulators (like Aspen, Pro II, etc.) and online advanced process control (APC) and real‐time optimization (RTO) applications. With the advance of artificial intelligence‐based data analytics a complete new generation of profit maximization tools and techniques are currently available. This book attempts to highlight some of these proven new generation tools, which slowly being introduced in progressive process industries. Adaptions of these new techniques in process industries need a mindset change of engineers and management. The real challenge becomes: how can a mindset change of management be made so that new generation techniques can be quickly applied to generate profit. How can these new generation tools and techniques be learnt and adapted quickly? The challenge boils down to: which methods should be selected, how can they be tailor‐made to fit them into specific applications, and how can they be implemented for specific circumstances?