Once a process model is developed for a major equipment, the next step is to optimize various process parameters so that efficiency or performance of that equipment is maximized. Due to cut‐throat competition in business, companies now want to reduce their operating costs by optimizing all their available resources, be it man, machine, money, or methodology. Optimization is an important tool that can be utilized to strike a proper balance so that profit can be maximized in the long run.
Since capital costs are already incurred for a running plant, optimization essentially boils down to minimization of operating costs for the operating plants. In running a chemical plant, there is a huge scope to optimize the operating parameters, like temperature, pressure, concentration, reflux ratio, etc., which give either higher profit through higher production or lower operating costs. There are many ways to optimize the operating conditions of reactors, distillation columns, absorbers, etc., to enhance their profitability.
Various recent stochastic optimization techniques, like genetic algorithm, differential evolution, particle swarm optimization, etc., have been used to optimize the developed data‐driven model.
3.1.12 Step 12: Modeling and Optimization of Industrial Reactors
A reactor is the only major equipment that can convert raw materials to a value‐added product. In chemical plants, the real value addition is done only in the reactor. Downstream separation units like distillation towers can be considered as cost centers because they consume energy to separate different products made in the reactor. All the downstream unit operations are for separation of the product and enrichment. Separation units consume energy and cost, whereas the reactor generates money by adding value to raw materials by converting them to a product. Hence, from a profit point of view, reactors are different from a downstream distillation column or other separation units.
In that respect, there is a huge potential impact of reactor optimization on overall plant profitability. The first step of reactor optimization is to know the governing kinetic equations. Industrial reaction kinetics are not known in many cases. A recent AI‐based technique, namely genetic programing, can be used to determine the kinetic equations of unknown industrial reactions. An artificial neural network (ANN) or genetic programing can then be used to model the industrial reactors. Once a reliable model is developed, various stochastic‐based optimization techniques can be used to optimize the reactor parameters to increase selectivity, yield, throughput, etc. In this step a modeling and optimization framework is made to derive more profit from the industrial reactor.
3.1.13 Step 13: Maximize Throughput of All Running Distillation Columns
Distillation is the largest separation unit in any refinery, petrochemicals or chemical plants. Though distillation is considered the most efficient separation process among other separation processes, a distillation column consumes a lot of energy in terms of steam in reboilers. Steam costs in various distillation columns constitute a large chunk of operation costs and in most cases are the second largest contributor to the cost component after raw material costs. Not only the operating cost but also large distillation columns and their presence in sheer large numbers in any CPI, contribute heavily to the plant's initial investment cost. In short, any CPI distillation unit contributes a very large percentage of both capital costs and operating costs. Therefore, any strategy to reduce capital and operating costs of distillation columns have a significant impact on plant profitability and the strategy can be seen as a multiplier, i.e. it can be applied to many distillation columns already present in the plants.
There are three main strategies by which more profit can be earned from an existing distillation column.
Strategy 1: Increase the feed in the distillation column to produce more products until limited by process constraints like flooding, entrainment, etc. The main constraints in the distillation column should be hydraulically stable so that it can produce an on‐spec product consistently at a higher load.
Strategy 2: Reduce reflux to reduce steam consumption. However, the constraint is that one must always produce a required purity of the product. For an energy intensive distillation column this is a major strategy and can be considered as a problem of process simulation.
Strategy 3: Exploit the variations in product purity or steam flow or feed flow. If there are many variations in product flow or product purity, then the column operation has to be stabilized by improving process control. Producing an ultra‐pure product (i.e. a purer product than its market specifications) has no economic benefit in the market. Impurity of the product should be at its allowable limit. Either an efflux rate reduction or feed increase has to be performed in order to reduce extra purity of product. This will increase profit. This problem can be tackled by APC.
Since a distillation operation can severely impact plant profitability, special attention is needed to optimize a running distillation column. In this step, various computation tools and modeling techniques are applied in a distillation column to maximize an economic benefit from it.
3.1.14 Step 14: Apply New Design Methodology for Process Equipment
To date, traditional methodology has been followed when designing a new process equipment. In the traditional method, equipment is designed based on its functionality. Cost is not taken as an objective function and minimization of the total cost is never taken as a design target in the traditional designing method. A moderate sized chemical plant uses 100 heat exchangers, distillation columns, reactors, etc., and minimization of their total cost, i.e both capital and operating costs, can be considered as a potential area to greatly increase profits. Cost minimization based on new design methodology can be applied during the grassroots design time and huge savings can be obtained. With the advent of commercial steady‐state simulators (started around the mid‐1980s) process equipment design took a giant leap forward and computer software/simulators marketed by Aspen, Hysis, and Pro‐II have been extensively used by designers around the world. With the faster computer, it is now possible to check billions of design options for a single piece of equipment and the lowest cost design can be finally selected. In this step a new stochastic optimization‐based methodology is developed for process equipment design. This new methodology uses minimization of the total cost as the design target while obeying all operational, safety constraints and equipment limitations. Various stochastic optimization algorithms are used to search the entire feasible space and find out the most cost‐effective design.
References
1 Energy and Process Optimization for the Process Industries (2013). In F.X.X. Zhu (Ed.), Energy and Process Optimization for the Process Industries. https://doi.org/10.1002/9781118782507.
2 Lahiri, S.K. (2017a). Assessment of regulatory base control layer in plants. In Multivariable Predictive Control (pp. 77–99). https://doi.org/10.1002/9781119243434.ch6.
3 Lahiri, S.K. (2017b). Introduction of model predictive control. In Multivariable Predictive Control (pp. 1–21). https://doi.org/10.1002/9781119243434.ch1.
4 Lahiri, S.K. (2017c). MPC implementation steps. In Multivariable Predictive Control (pp. 55–62). https://doi.org/10.1002/9781119243434.ch4.
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