Linear and Generalized Linear Mixed Models and Their Applications

edge of both engineering process control and statistics. A minimum of a master’s level background is preferred (in the opinion of the reviewer). The calculus and matrix algebra, in addition to the advanced terminology, will eliminate any misunderstandings with respect to prerequisites. In the typical format of a collection of contributions from both academicians and practicing electrical and industrial engineers, the continuity is somewhat lacking. This is regrettably often inevitable, due to the difference of writing styles of the authors. The issue is exacerbated by the differences in chapter formats. Some have (often excellent) cases, while others present mostly theory. Much of the book is (not surprisingly) devoted to benchmarking, a topic of extreme familiarity to quality professionals, but only relatively recently introduced to process engineering applications. Benchmarking techniques are delineated based on data-driven and model-driven techniques, and on single input– single output and multiple input–multiple output systems. The general benchmarking coverage culminates with a chapter-long simulation study of a divided wall column distillation process. Other chapters cover economic auditing of control systems, locating sources of disturbances, technical issues with control-system benchmarking, and new research directions. The strongest of these is the economic auditing (in Chap. 2), almost three-fourths of which is devoted to a case study of an oil-production platform. The mix of managerial and engineering methodology and verbiage is optimal. The weakest chapter covers the location of disturbances. Giving short shrift to statistical techniques, the authors ignore much relevant literature in this vital area. The case study is of little use for elucidation; it is too short, and uses a small subset of available methodology. The interested reader is referred to Blanke, Kinnaert, Lunze, and Staroswiecki (2006) for a thorough coverage of locating disturbances in control systems. The final chapter is definitely tailored for the academician interested in cutting-edge process-control scholarship. The proposed new research directions are no doubt relevant, covering new performance metrics, algorithms, and performance analysis. But this largely amounts to straining at gnats, since there is a wealth of literature available on many applicable statistically-based techniques. The most salient is algorithmic process control, and the general integration of statistical process control with engineering process control. Montgomery (2004) offers a superb introduction and coverage of the major corpus in this subject. Overall, the book accomplishes its ostensible purpose. The mix of theory and application will most likely appeal more to the academic reader than the industrial practitioner. The caveat here is that the academician in question needs a thorough background in control theory. This is not an introductory text for interlopers wishing to break into process control-theoretic research. Despite certain shortcomings, the current piece is a nice addition to the process control library. Any interested academic researchers from this area should strongly consider purchasing it. Conversely, most practitioners will find it too pedantic. This is hardly an indictment of this text, as the entire series seems to target the ivory tower. A separate industry-oriented series anyone?