Shuhaimi Mahadzir

Artificial Neural Network for Anomalies Detection in Distillation Column

Early detection of anomalies can assist to avoid major losses in term of product degradation, machines’ damages as well as human health issues. This research aims to use Artificial Neural Network to recognize anomalies in the distillation column. The pilot scale distillation column for the ethanol-water system is selected for the study. Faults are generated by variation in feed rate, feed composition and reboiler duty using Aspen Plus® dynamic simulation. The effect of these faults on process variables i.e. changes in distillate and bottom composition, distillate and bottom temperature, bottom flow rate, and the pressure drop is observed. The network is trained using back propagation algorithm to determine root mean square error (RMSE). Based on RMSE minimization, the (6-8-6) net serves as the best choice for the case studied for efficient fault detection. The presented techniques are general in nature and easily applicable to various other industrial problems.

Profit optimization for chemical process plant based on a probabilistic approach by incorporating material flow uncertainties

This paper reports how the economic performance of a chemical process plant is affected by material flow uncertainties from the plant inlet and outlet. Two chance-constrained optimization models were proposed. The models were tested using case studies of an existing gas processing plant. Profit optimization for the case studies was made with respect to the reliability of holding the process constraints at a certain confidence level [0.5, 1]. The optimal profit change for uncertainty from the plant inlet within the confidence interval [0.96, 1] was 86%. On the other hand, the optimal profit change for uncertainty from the plant outlet was only 2% for the same confidence level interval considered. This suggests that the uncertainty from the plant inlet has a major impact on the overall economic performance of the plant. Sensitivity analysis showed how uncertain parameters from both plant sides can affect the overall profit significantly.

Analyzing the effects of uncertainties on the economic performance of a chemical process plant using a probabilistic optimization technique

This work presents a mathematical optimization model based on a probabilistic approach to clearly identify the effects material flow uncertainties, from both plant inlet and outlet, on the overall economic performance of a plant. Accordingly, two case studies have been taken for an existing gas processing plant. Each profit analysis for the case studies is made with respect to the reliability of holding the process constraints. Based on this, a user-defined probability or confidence level has been assigned for the constraints between the ranges of 50% to 100%. There is a significant profit change of more than 85.8 % for the plant inlet uncertainty case as the confidence level increase from 96% to 100%. However, the profit change for uncertainty from the plant outlet is much less (1.93%) compared to that of the plant inlet for the same confidence level interval taken. The result indicates that much emphasis should be given to those uncertainties from the plant inlet which has a major effect in the overall economic performance of the plant performance.

In situ transesterification of non-edible oil in the presence of cetyltrimethylammonium bromide

In situ transesterification of Jatropha curcas l. seed with base catalysed alcohol in the presence of cetyltrimethylammonium bromide (CTMAB) as phase transfer catalyst (PTC) was investigated. The yield of fatty acid methyl esters (FAME) increased from 49.7 w% to 89.2 w% in the presence of CTMAB as PTC at 240 minutes of reaction time. Similarly, the yield of fatty acid ethyl esters (FAEE) increased from 84.7 w% to 99.5 w% in the presence of CTMAB as PTC at 150 minutes of reaction time. The quality of biodiesel fuel conforms to the standards of ASTM D6751 and EN-14214.

Modeling and optimization of water-based polygeneration system

Abstract The impact of climate change, particularly drought, places an enormous pressure on the synthesis of process systems that use freshwater resources efficiently. This paper presents the synthesis of water-based polygeneration system to minimize freshwater consumption. A comprehensive model is formulated through a superstructure, featuring the possible configurations of simultaneous heat and power generations, re-circulating cooling water system, wastewater treatment options, as well as reaction and separation technologies for chemical production. Process units are modeled using Aspen Hysys 2006. A case study on the synthesis of optimum water polygeneration system is developed for an ethylene glycol production. The superstructure model is a mixed integer non-linear programming problem (MINLP) consisting of 305 equations, 326 variables and 20 binary variables. The objective of the model is to minimize freshwater make-up subject to 85 constraints. The model is subsequently solved using DICOPT++ in GAMS 20.7 with the optimum solution showing nearly 50% savings of freshwater consumption.

Reaction Kinetics Study for Microwave Energy Pretreated Jatropha Curcas L In-Situ Transesterification

In the present work, in-situ methanolysis of microwave irradiation heat pre-treated Jatropha curcas l oil in the presence of alkaline benzyltrimethylammonium hydroxide (BTMAOH) as a phase transfer catalysis (PTC) was investigated. Combined use of microwave heat pretreatment of Jatropha curcas l seed particles and alkaline BTMAOH as a PTC drastically reduced the reaction time from 240 to 30 min. The yield of fatty acid methyl ester (FAME) was also increased from 49.7 to 93.5 %. Reaction kinetics was investigated to study the extent of reaction rate. Reaction kinetic study demonstrated that the order of the reaction is a first order reaction at all reaction temperature under investigation. Activation energy of the reaction was found to be 21,641 J/mole. Combined use of microwave heat treatment of Jatropha curcas l seed particles and PTC is promising to enhance the reaction rate of in-situ methanolysis.

Operation System Optimization

This chapter presents a new advancement in energy minimization and CO2 mitigation research. It describes the development of a combined technique to simultaneously synthesize energy recovery network and offers fuel switching options to satisfy CO2 emission reduction target using graphical and mathematical programming approaches. The approach is illustrated using petroleum refinery and palm oil refining process as the case study. The application of this technique yields significant CO2 emission reduction with short payback period with or without clean development mechanism (CDM).