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

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


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profit in a plant. In this step the PMP engineer usually assesses the current operation, analyzes historical data, understands the various safety and process constraints and equipment limitations, etc. A functional design aims to identify all the existing opportunities to increase the plant profit (Lahiri, 2017c). In this step, the PMP engineer formulates various profit improvement strategies and identifies all potential applications where application of data analytics and modeling and optimization techniques can be applied to improve profit. A preliminary feasibility study is undertaken to identify whether an APC application can be implemented. In this step, an overall idea and forward path is made regarding which PMP application will be used and where to tap into the profit increase opportunity. A functional design step basically provides a map of every opportunity and which methods will be used to exploit a particular opportunity. The success of the profit maximization will greatly depend on how the functional design is formulated in order to tap into the potential margins available in the process. This step requires synergy between expertise and experience of the domain engineer or plant process engineer and the PMP engineer.

      3.1.6 Step 6: Develop an Advance Process Monitoring Framework by Applying the Latest Data Analytics Tools

      3.1.7 Step 7: Develop a Real‐Time Fault Diagnosis System

      Disruption of whole operations due to malfunctions of various process equipment is very common in chemical plants. In the worst case, due to malfunction of compressors, distillation columns, a process instrument, the electrics of a whole plant tripped and a large amount of money was lost. Today's chemical plants are so complicated and interrelated between various sections that once a plant trips, the whole process becomes destabilized and it takes 1 or 2 days or more to stabilize the process and continue on‐spec production. This not only reduces the profit due to a lower production rate but also represents a huge loss due to flaring/draining, production of off‐spec production, etc. Early detection of a fault or equipment malfunction can help to take corrective or preventive actions at their incipient stage and thus avoid the financial loss. Fault diagnosis of equipment or a process is an online real‐time system, which continuously monitors various equipment‐related data (say temperature, pressure, vibrations, etc.) and sends an early alert signal when a fault is detected. This early alert is triggered before the fault actually disrupts the process. This will help the concerned engineer to focus on the particular fault and take preventive action to avoid process disturbance. In most cases where a fault is detected at its incipient stage the operator will be able to avoid trips and reduce the financial loss associated with a plant trip. It is absolutely necessary nowadays to implement a fault detection system in running a plant to increase its on‐stream factor, i.e. running hours.

      3.1.8 Step 8: Perform a Maximum Capacity Test Run

      3.1.9 Step 9: Develop and Implement Real‐Time APC

      PID control formed the backbone of a control system and is found in a large majority of CPIs. PID control has acted very efficiently as a base layer control over many decades. However, with the global increase in competition, process industries have been forced to reduce the production cost and need to maximize their profit by continuous operation in the most efficient and economical manner.

      Most modern chemical processes are multivariable (i.e. multiple inputs influence the same output) and exhibit strong interaction among the variables (Lahiri, 2017b).

      In a process plant, it is only seldom that one encounters a situation where there is a one‐to‐one correspondence between manipulated and controlled variables. Given the relations between various interacting variables, constraints, and economic objectives, a multi‐variable controller is able to choose from several comfortable combinations of variables to manipulate and drive a process to its optimum limit and at the same time achieve the stated economic objectives. By balancing the actions of several actuators that each affect several process variables, a multi‐variable controller tries to maximize the performance of the process at the lowest possible cost. In a distillation column, for example, there can be several tightly coupled temperatures, pressures, and flow rates that must all be coordinated to maximize the quality of the distilled product.

      Advance process control (APC) is a method of predicting the behavior of a process based on its past behavior and on dynamic models of the process. Based on the predicted behavior, an optimal sequence of actions is calculated. The first step in this sequence is applied to the process. Every execution period a new scenario is predicted and corresponding actions calculated, based on updated information.

      The real task of APC is to ensure that the operational and economic objectives of the plant are adhered to at all times. This is possible because the computer is infinitely patient, continuously observing the plant and prepared to make many, tiny steps to meet the goals (Lahiri, 2017b).

      APC has established itself as a very efficient tool to optimize the process dynamically, minimize variations of key parameters, and push the plant to multiple constraints simultaneously and improve the profit margin on a real‐time basis.

      3.1.10 Step 10: Develop a Data‐Driven Offline Process Model for Critical Process Equipment


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