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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?

1.2 Who is This Book Written for?

      However, there are no dedicated effective books available to discuss a basic roadmap to utilize various intelligent tools and techniques, provide practical methods to implement them on the shop floor, and explain industrial application procedures.

      This book has been written to fill this gap with the following people in mind: practicing process or chemical engineers, production engineers, supervisors, and senior technicians working in chemical, petrochemical, pharmaceuticals, paper and pulp, oil and gas companies and petroleum refineries across the globe. This book will also become particularly useful for large numbers of managers, general managers, top level senior executives, and senior technical service consultants whose main jobs include strategic planning and implementation of various optimization projects to increase profit in chemical process industries. Undergraduate and postgraduate chemical engineering students and business students who want to pursue careers in the chemical field will also greatly benefit from this book. The book is aimed at providing various intelligent computational tools to engineers and managers working in CPI who face challenges and are looking for new ways to increase profit in running chemical plants. This book aims to convey concepts, theories, and methods in a straightforward and practical manner.

This book provides engineers with all practical aspects of profit maximization projects, as well as practical advice on how to derive maximum benefits from running plants.

      The book will present various intelligent computation techniques covering profit optimization strategy, application methodology, supporting structures, and assessment methods. In short, it will describe completely new ways and techniques to maximize profit for process plants and how to sustain profit improvement benefits.

      Short on background theory and long on step‐by‐step application procedures, it covers everything plant process engineers and technical managers need to know about identifying, building, deploying, and managing profit improvement applications in their companies. Readers are able to take away new ways to increase profit in their current plant, background computational tools and techniques for identifying profit improvement opportunities, and analysis, optimization, and monitoring procedures that are required to identify, assess, implement, and sustain profit improvement opportunities.

1.3 What is Profit Maximization and Sweating of Assets All About?

      In chemical process industries (CPIs), profit maximization is attained in many fragmented ways. Energy managers in a plant try to increase profit by increasing energy efficiency, production engineers increase profit by pushing the plant to its highest possible capacity, control engineers try to optimize the plant in real time by advance process control, maintenance engineers try to maximize the critical single line equipment availability by doing proper preventive and predictive maintenance, reliability engineers try to reduce the failure rate by proper inspection, the human resource (HR) department tries to reduce manpower cost and increase employee's productivity, safety engineers try to minimize the incidence and accident rate, etc., and many people try to maximize profit in a multi‐dimensional fragmented way. However, all of the above approaches are not independent but are deeply interrelated and sometimes conflicting. For example, running equipment beyond its design limit for maximization of plant capacity will definitely increase its failure rate. Therefore, profit maximization is an approach that sees all these conflicting attempts in a holistic way and evaluates the strategy that will maximize profit of the plant in the long run and sustain it.

      In simple terms, profit maximization means maximize dollar per hour generation from the plant and make sure that this is sustained. In mathematical terms,

       Maximize

       Profit generation in $/h terms from the plant

       Subject to constraints: all process and safety constraints need to be honored and all equipment limitations should not be violated

      Some common ways to maximize profit are (but not limited to) (Lahiri, 2017a):

       Maximize plant throughput while obeying all operational and safety limits imposed by the designer.

       Minimize raw material and utility consumption.

       Reduce production costs by maximizing process efficiency (like catalyst selectivity, yield).

       Increase plant and process equipment reliability while obeying all design and safety limitations so that the profit‐making production process can be sustained for longer periods, etc. In still other cases, there is a tradeoff between increased throughput and decreased process efficiency and so process optimization is needed.

There are many multi‐faceted dimensions of the profit maximization project. The profit maximization project (PMP) involves all the activities to increase profit in the plant. As the scope is vast and multi‐disciplinary, this book only addresses some of the ways that are related to chemical engineering/process engineering and suggest some alternative new ways to generate profit, some of which are given in Figure 1.2 below.

      Critically assess current plant operation and identify and exploit the opportunities.

For any process unit, there are various constraints and limits, as shown in Figure 1.1 which stops the plant from continuously increasing capacity (Lahiri, 2017b).

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