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It has been recognised that the effect of dynamics on process economics is heavily dependent on the implemented controller, and that the physically attainable controller performance is limited by the choice of controlled and manipulated variables. This work considers the systematic selection of economically optimal square regulatory control structures from the set of all possible measurements and manipulated variables. This is a combinatorial problem, as typical processes have millions of potential control structures, where a control structure consists of a set of variables to be controlled and manipulated, but does not include a control law relating the variables.The solution of such a synthesis problem requires an analysis method. Historical approaches to control structure evaluation consider "controllability indices", and their trade off against steady state economics, whereas the economic indicators described in this thesis allow direct comparison and trade off of control structures against steady state economics.Assuming a method for assessing control structure economics, a hierarchical decomposition of the design problem into steady state and dynamic problems is demonstrated.A method for assessing the effect of dynamics on process economics for a specific control structure without tuning a controller is developed, based on linearised process models, linear systems theory and constraint control concepts. This assessment is extended to consider the control structure selection problem, resulting in a mixed integer linear programming (MILP) outer approximation to a mixed integer nonlinear programming (MINLP) problem.A mixed integer nonlinear optimal control problem is posed for the problem of selecting an optimal multiloop proportional-integral process control structure.The above problems are formulated as standard optimisation problems, and solved by appropriate techniques. Experience with the methods is presented as a set of case studies.Finally the novel features and applications of the work are discussed along with potential areas of further research.