Biosurfactant production through a fermentation process involving the biodegradation of soybean oil refining wastes was studied. Pseudomonas aeruginosa MR01 was able to produce extracellular biosurfactant when it was cultured in three soybean oil refinement wastes; acid oil, deodorizer distillate and soapstock, at different carbon to nitrogen ratios. Subsequent fermentation kinetics in the three types of waste culture were also investigated and compared with kinetic behavior in soybean oil medium. Biodegradation of wastes, biosurfactant production, biomass growth, nitrate consumption and the number of colony forming units were detected in four proposed media, at specified time intervals. Unexpectedly, wastes could stimulate the biodegradation activity of MR01 bacterial cells and thus biosurfactant synthesis beyond that of the refined soybean oil. This is evident from higher yields of biodegradation and production, as revealed in the waste cultures (Ydeg|(Soybean oil) = 53.9 % < Ydeg|(wastes) and YP/S|(wastes) > YP/S|(Soybean oil) = 0.31 g g(-1), respectively). Although production yields were approximately the same in the three waste cultures (YP/S|(wastes) =/~ 0.5 g g(-1)), microbial activity resulted in higher yields of biodegradation (96.5 ± 1.13 %), maximum specific growth rate (μ max = 0.26 ± 0.02 h(-1)), and biosurfactant purity (89.6 %) with a productivity of 14.55 ± 1.10 g l(-1), during the bioconversion of soapstock into biosurfactant. Consequently, applying soybean oil soapstock as a substrate for the production of biosurfactant with commercial value has the potential to provide a combination of economical production with environmental protection through the biosynthesis of an environmentally friendly (green) compound and reduction of waste load entering the environment. Moreover, this work inferred spectrophotometry as an easy method to detect rhamnolipids in the biosurfactant products.

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A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields.

In many cases, multiple-fault diagnosis of plant-wide systems based on steady-state data is impossible. To solve this problem, a new diagnosis strategy based on neural networks has been proposed. In the suggested framework, the neural network is used as the diagnoser trained by a hybrid set of steady and dynamic characteristic data of the system. The dynamic characteristic data include overshoot and undershoot values of measured variables and their corresponding occurrence times. To evaluate its performance, the proposed scheme was used in the diagnosis of the concurrent faults of the Tennessee Eastman (TE) process. Various combinations of concurrent faults were considered in this assessment. The results indicate the generality, flexibility, and accuracy of the proposed algorithm such that it is capable of diagnosing various combinations (from single to sextuple) of simultaneous faults, whereas the other diagnosing methods used for the TE process are capable of distinguishing at most three simultaneous faults.