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
The most important step in developing a profit management solution to optimize a process is to be able to measure what process performance looks like against a reasonable set of benchmarks. This involves capturing process performance and relevant cost data related to the process and organizing it in a way that allows operations to quickly identify where the big cost consumers are and how well they are doing against a consumption cost target that reflects the current operations. Only then it is possible to do some analysis to determine the cause of deviations from a target and take appropriate remedial action. For this purpose, the concept of key profit indicators is introduced (Zhu, 2013). In this step, a dashboard is prepared for a monitoring purpose where all the key profit indicators along with their target values are shown in real‐time. This dashboard enables an operation engineer to quickly identify any deviations of cost performance of the process and take necessary preventive and corrective actions. In this step an advance process monitoring system is developed by using latest data analytics, like clustering, fault tree, a cause and effect diagram, artificial neural network, etc. This monitoring framework helps to visualize the whole process and its performance against targets and quickly highlights any deviations of performance or improvement opportunities.
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
Capacity expansion is the single most important strategy used to increase profit. Capacity expansion means an increase in the plant throughput (measured by product flow in MT/h) over and above its name plate capacity. There are various design margins available in various process equipment. A maximum capacity test run is a process that systematically increases plant capacity and exploits these margins. It is possible to increase plant capacity by 5–10% without any major investment and simply utilizing the hidden margins available in installed equipment. The main idea behind a maximum capacity test run is to slowly push the plant capacity until it reaches major equipment or process limitation. This is a very important tool used to increase the plant profit by systematically running the plant at a higher capacity.
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
If data is the new oil in a modern chemical industry, then the data‐driven modeling technique is a combustion engine. Industrial chemical processes are complex to understand and difficult to model. However, to increase profit and run the chemical processes at their optimum, availability of a reliable mathematical model is very crucial. Chemical engineering has not developed to accurately develop the phenomenological model of complex chemical processes like catalytic reactors, adsorption, etc. As the chemical industry has a huge amount of historical operating data available in its server, a data‐driven process modeling technique has emerged as a viable option. How to use various data‐driven modeling techniques to increase profit in plants is a key challenge. Data‐driven modeling is an important tool used to increase profit by modeling major process equipment and then optimizing various process parameters associated with it so that performance of the equipment is maximized. In this step, big ticket items like a reactor, major distillation columns, and major compressors are identified where developing a model and