Profit Maximization Techniques for Operating Chemical Plants. Sandip K. LahiriЧитать онлайн книгу.

      1 Chapter 1Figure 1.1 Various constraints or limits of chemical processesFigure 1.2 Optimum operating point versus operator comfort zone

      2 Chapter 2Figure 2.1 Developing stages of the chemical industryFigure 2.2 Three major ways digital transformation will impact the chemical ...Figure 2.3 Three major impact areas where advance analytic tools will help t...Figure 2.4 Different components of the insights value chainFigure 2.5 Overview of the insights value chain upstream processes (A–B) and...Figure 2.6 Data science is an iterative process that leverages both human do...

      3 Chapter 3Figure 3.1 Different steps in profit maximization project (PMP) implementati...

      4 Chapter 4Figure 4.1 Different ways to maximize the operating profit of chemical plant...Figure 4.2 Schematic diagram of a glycol plantFigure 4.3 Steps to map the whole plant in monetary terms and to gain insigh...Figure 4.4 Representing the whole plant as a black boxFigure 4.5 Mapping the whole plant in monetary termsFigure 4.6 Break‐up of the total cost of productionFigure 4.7 Cost of raw materialFigure 4.8 Cost of different utilities (USD/h)Figure 4.9 Cost of different chemicals (USD/h)Figure 4.10 Variations of profit margin (USD/h) throughout the yearFigure 4.11 Variations of profit margin (USD/MT of product) throughout the y...Figure 4.12 Variations of production cost (USD/MT) throughout the yearFigure 4.13 Variations of MEG production (MT/h) throughout the year

      5 Chapter 5Figure 5.1 Five‐step process of a key parameter identificationFigure 5.2 Queries normally asked to perform a process analysis and economic...Figure 5.3 Major six categories of limitations in a plant to increase profit...Figure 5.4 Some examples of process limitationsFigure 5.5 Some examples of equipment limitationsFigure 5.6 Examples of instrument limitationsFigure 5.7 Guideline questionnaires to initiate the discussion with plant pe...Figure 5.8 Various causes of catalyst selectivity increase

      6 Chapter 6Figure 6.1 Comparison of daily actual profit (sorted) versus best achieved p...Figure 6.2 Daily opportunity loss in million US$ for one year of operationFigure 6.3 Cumulative opportunity loss in million US$ for one year of operat...

      7 Chapter 7Figure 7.1 Advantage and disadvantage of the first principle‐based modelFigure 7.2 Advantages and disadvantages of data‐driven modelsFigure 7.3 Advantages and disadvantages of the grey modeling techniqueFigure 7.4 Advantages and disadvantages of the hybrid modeling techniqueFigure 7.5 Typical pseudo code of a back‐propagation algorithmFigure 7.6 Architecture of a feed‐forward network with one hidden layer (Lah...Figure 7.7 Steps followed in data collection and data inspectionFigure 7.8 Task performed in the data pre‐processing and data conditioning s...Figure 7.9 Two main univariate approaches to detect outliersFigure 7.10 Guidelines for selection of the relevant input output variables...Figure 7.11 Relation between catalyst selectivity and promoter concentration...Figure 7.12 Actual selectivity versus ANN model predicted selectivityFigure 7.13 Prediction error percent between actual selectivity and predicte...Figure 7.14 Plot of actual selectivity versus predicted selectivity for test...Figure 7.15 ANN model performance for testing and training dataFigure 7.16 Different ANN algorithms developed by different scientists in th...Figure 7.17 Different activation functions used in an ANN

      8 Chapter 8Figure 8.1 Different minimum values of a function depending on different sta...Figure 8.2 Principle features possessed by a genetic algorithmFigure 8.3 Foundation of the genetic algorithmFigure 8.4 Five main phases of a genetic algorithmFigure 8.5 Mechanism of crossoverFigure 8.6 Calculations steps performed in DE (Babu, 2004)Figure 8.7 Schematic diagram of DEFigure 8.8 Calculation sequence of a simulated annealing algorithm

      9 Chapter 9Figure 9.1 Cause and effect relationship of a steam increase in the distilla...Figure 9.2 KPI‐based process monitoringFigure 9.3 Projection of a three‐dimensional object on a two‐dimensional pla...Figure 9.4 Projection of a three‐dimensional object on a two‐dimensional pri...Figure 9.5 Projection of data towards a maximum variance planeFigure 9.6 Steps to calculating the principal componentsFigure 9.7 Normal and abnormal operating zones are clearly different when pl...Figure 9.8 Trends of the first principal componentFigure 9.9 Variance explained by the first few principal componentsFigure 9.10 Front end to detect abnormality in the reciprocating compressor...Figure 9.11 Normal and abnormal data projected onto the first two and first ...

      10 Chapter 10Figure 10.1 New business challenges versus improve performanceFigure 10.2 Pyramid of a process monitoring systemFigure 10.3 Fault diagnosis systemFigure 10.4 Characteristics of an automated real–time process monitoring sys...Figure 10.5 Concerns when building an effective fault diagnosis systemFigure 10.6 Different requirements of different stakeholders from fault diag...Figure 10.7 Summary of user perspective and challenges to build an effective...Figure 10.8 Principal component plotFigure 10.9 Schematic of an ethylene oxide reactor and its associated unitFigure 10.10 EO reactor process parameters along with a schematicFigure 10.11 Various challenges to develop an EO reactor fault diagnosisFigure 10.12 Chloride versus catalyst selectivity plotFigure 10.13 PCA scores plot, T ² plot, and residual plotFigure 10.14 Interface between a data historian and a dedicated PC loaded wi...Figure 10.15 Contribution plots of 15 variablesFigure 10.16 Dynamic movement of the reactor status from the normal zone to ...Figure 10.17 Steps to build a PCA‐based fault diagnosis systemFigure 10.18 Actual versus ANN model predicted selectivity and equivalent et...Figure 10.19 Integrated robust fault diagnosis system

      11 Chapter