In recent years, batch processes have been getting more attention due to their suitability for the production of small volume, high value added products. The flexibility of batch plants allows the production of different products within the same facility which mandates equipment sharing. Batch manufacturing is typically used in the pharmaceutical, polymer, food and specialty chemical industries as demands for such products are highly seasonal and are influenced by changing markets. Despite the advantage of batch plants being flexible, they also pose a challenging task to design, synthesize and operate, compared to their continuous counterparts. The profitability of these batch plants is highly dependent on the way the synthesis, design and operation is optimized. Since different types of resources (raw materials, equipment, utilities and manpower) need to be shared by a number of process operations to produce a variety of products, modeling and optimizing the design and operation of batch plants are important for economic benefits. The growing awareness of civil society for the environment and the resulting regulations introduced by national states have resulted in chemical industries considering process integration to reduce their energy and process water requirements. Energy optimization and the optimization of water use have mainly been treated as separate problems in literature. The batch production schedules resulting from each of these formulations do not guarantee that the plant is operated optimally. Consequently, it is required to develop a formulation that caters for opportunities that exist for both wastewater minimization and energy integration. This may result in production schedules that improve the operation of the batch plant when compared to optimizing water and energy separately.
Presented in this thesis is a mathematical technique that addresses optimization of both water and energy, while simultaneously optimizing the batch process schedule. The scheduling framework used in this study is based on the formulation by Seid and Majozi (2012). This formulation has been shown to result in a significant reduction of computational time, an improvement of the objective function and leads to fewer time iii
points required to solve the scheduling problem. The objective is to improve the profitability of the plant by minimizing wastewater generation and utility usage. From a case study it was found that through only applying water integration the total cost is reduced by 11.6%, by applying only energy integration the total cost is reduced by 29.1% and by applying both energy and water integration the total cost is reduced by 34.6%. This indicates that optimizing water and energy integration in the same scheduling framework will reduce the operating cost and environmental impact significantly.
This thesis also presents a mathematical model for design and synthesis of batch plants. The conceptual design problem must determine the number and capacity of the major processing equipment items, pipe connections and storage tanks so as to meet production objectives at the lowest possible capital and operating cost. A recent robust scheduling model based on continuous-time representation is used as a platform for the synthesis and design problem. An improved objective value (revenue) of 228.6% is obtained by this work compared to the recent published models for the design and synthesis problem. Compared with other formulations, the formulation presented in this thesis gives a smaller size mathematical model that required less binary variables, continuous variables and constraints. The presented model also considers costs that arise from the pipe network and consequently, determines the optimal pipe network which should exist between different pieces of equipment. Finally, the medium-term scheduling problem for a multiproduct batch plant is addressed. The intractability of the short-term scheduling models when directly applied to the medium-term scheduling problems is solved by applying a decomposition method. The decomposition method has two level mathematical models. The first level determines the type of products and their amount to be produced in each scheduling subproblem to satisfy the market requirement. The second level determines the detailed sequencing of tasks for the tractable size of the subproblems. The recently published robust short-term scheduling model based on continuous time is extended for solving the scheduling supbroblems of the second level decomposition model. The model is applied in solving the medium-term scheduling problem of a pharmaceutical facility specializing in animal vaccines using the actual plant data. The model effectively solved a makespan minimization problem for the medium-term scheduling horizon of almost 13 weeks.