This thesis describes the development and application of a new systematic modelbased methodology for performing integrated process design and controller design (IPDC) of chemical processes. The new methodology is simple to apply, easy to visualize and efficient to solve. Here, the IPDC problem that is typically formulated as a mathematical programming (optimization with constraints) problem is solved by the so-called reverse approach by decomposing it into four sequential hierarchical sub-problems: (i) pre-analysis, (ii) design analysis, (iii) controller design analysis, and (iv) final selection and verification. Using thermodynamic and process insights, a bounded search space is first identified. This feasible solution space is further reduced to satisfy the process design and controller design constraints in sub-problems 2 and 3, respectively, until in the final sub-problem all feasible candidates are ordered according to the defined performance criteria (objective function). The final selected design is then verified through rigorous simulation. In the pre-analysis sub-problem, the concepts of attainable region and driving force are used to locate the optimal process-controller design solution in terms of optimal condition of operation from design and control viewpoints. The targets for the design-control solution are defined at the maximum point of the attainable region and driving force diagrams. Defining the targets at the maximum point of the attainable region and driving force diagram ensure the optimal solution not only for the process design but also for the controller design. From a process design point of view at these targets, the optimal design objectives can be obtained. Then by using the reverse solution approach, values of design-process variables that match those targets are calculated in Stage 2. Using model analysis, controllability issues are incorporated in Stage 3 to calculate the process sensitivity and to pair the identified manipulated variables with the corresponding controlled variables. From a controller design point of view, at targets defined in Stage 1, the sensitivity of controlled variables with respect to disturbances is at the minimum and the sensitivity of controlled variables with respect to manipulated variables is at the maximum. Minimum sensitivity with respect to disturbances means that the controlled variables are less sensitive to the effect of disturbances and maximum sensitivity with respect to manipulated variables determines the best controller structure. Since the optimization deals with multicriteria objective functions, therefore, in Stage 4, the objective function is calculated to verify the best (optimal) solution that satisfies design, control and economic criteria. From an optimization point of view, solution targets at the maximum point of the attainable region and driving force diagrams are shown the higher value of the objective function, hence the optimal solution for the IPDC problem is verified. While other optimization methods may or may not be able to find the optimal solution, depending on the performance of their search algorithms and computational demand, this method using the attainable region and driving force concepts is simple and able to find at least near-optimal designs (if not optimal) to IPDC problems. The developed methodology has been implemented into a systematic computer-aided framework to develop a software called ICAS-IPDC. The purpose of the software is to support engineers in solving process design and controller design problems in a systematic and efficient way. The proposed methodology has been tested using a series of case studies that represents three different systems in chemical processes: a single reactor system, a single separator system and a reactor-separator-recycle system.