*5.2.2 Mathematical complexity*

Analysis, like the problem definition phase, the targeting phase (approximations and heuristic rules that fail), likewise the design and optimization phase (multiple scenarios, topology traps, etc.). In theory, Mathematical Programming overcomes of these limitations however, a number of the corresponding models are extremely difficult to resolve. Moreover, the problem itself is incredibly difficult to formulate as economic trade-off predictions for the full life-cycle of a plant and develop feasible designs which might fulfill all trade-offs and provision for the inclusion of all needed modifications from start to end into a single achievable design are the parameters that cannot be easily obtained in mathematical equations. Even for a given process the insight from the prevailing design will be very difficult to formulate mathematically. If we utilize approach where all processes-utility-waste recovery are attempted to be unified which include parameters like plot plan modification's economical evaluation etc. the formulations are tremendously difficult to obtain. This is often another excuse to focus and develop sustainable design through evolutionary-approach instead of with the more time-consuming revolutionary-approach. Finally, it should be instructive to say that Mathematical Programming could provide a framework but very difficult to urge Automatic Design, which implies that time is needed to induce solutions with experience-based directions for high-level decisions [14–16].
