Search

In this paper, we determined the optimal flow rate trajectory during the loading phase of a mAb capture column. For this purpose, a multi-objective function was used, consisting of productivity and resin utilization. Several general types of trajectories were considered, and the optimal Pareto points were obtained for all of them. In particular, the presented trajectories include a constant-flow loading process as a nominal approach, a stepwise trajectory, and a linear trajectory. Selected trajectories were then applied in experiments with the state-of-the-art protein A resin mAb Select PrismATM, running in batch mode on a standard single-column chromatography setup, and using both a purified mAb solution as well as a clarified supernatant. The results show that this simple approach, programming the volumetric flow rate according to either of the explored strategies, can improve the process economics by increasing productivity by up to 12% and resin utilization by up to 9% compared to a constant-flow process, while obtaining a yield higher than 99%. The productivity values were similar to the ones obtained in a multi-column continuous process, and ranged from 0.23 to 0.35 mg/min/mL resin. Additionally, it is shown that a model calibration carried out at constant flow can be applied in the simulation and optimization of flow trajectories. The selected processes were scaled up to pilot scale and simulated to prove that even higher productivity and resin utilization can be achieved at larger scales, and therefore confirm that the trajectories are generalizable across process scales for this resin.

With continued development of integrated and continuous downstream purification processes, tuning and optimization become increasingly complicated with additional parameters and codependent variables over the sequence. This article offers a novel perspective of nonlinear optimization of integrated sequences with regard to individual column sizes, flow rates, and scheduling. The problem setup itself is a versatile tool to be used in downstream design which is demonstrated in two case studies: a four-column integrated sequence and a continuously loaded twin-capture setup with five columns.

The Python Module Coupler (PyMoC) is a tool for co-simulation of Aspen Plus Dynamics modules that together make up an overall process flowsheet. The tool requires only user input in the form of file paths to Aspen Plus Dynamics modules, and it is able to automatically make the required connections there between, and keep track of the simulation whilst updating the streams regularly. This contribution briefly discusses the implementation and mechanisms of PyMoC, and then applies it to a multi-module, single-shooting constrained optimization problem, where an industrial set-up consisting of an evaporator system coupled to a distillation column is studied. This serves as a showcase of PyMoC's functionality and usability, as well as its potential in serving as a helpful tool for practitioners of model-based studies who could benefit from modularizing their models. Utilizing PyMoC for this purpose, the optimization results indicate that the operating costs induced from the steam consumption can be reduced by 54% compared to a nominal operating case, but a holistic, full-process study is necessary to understand the full set of possibilities, causes, and effects.

This study presents a deterministic, lumped model to simulate mesoscale sustainable drainage systems (SuDS) based on a conceptualization of the stormwater control measures (SCMs) making up the system and their influence on the runoff process. The conceptualization mainly relies on parameters that are easily quantifiable based on the physical characteristics of the SCMs. Introducing a nonlinear reservoir model at the downstream end of the SuDS results in a fast model that can realistically describe the runoff process at low computational cost. Modelled hydrographs for the study area in Malmo, Sweden, matched data with regard to the overall shape of the hydrograph as well as the peak discharge and lag time. These output parameters are critical factors to be considered in the design of large systems consisting of mesoscale SuDS. The algebraic foundation of the developed model makes it suitable for large-scale applications (e.g., macroscale), where the simulation time is a decisive factor. In this respect, city-wide optimization studies for the most efficient location and implementation of SuDS are substantially accelerated due to fast and easy model setup. Moreover, the simplicity of the model facilitates more effective communication between all the actors engaged in the urban planning process, including political decision makers, urban planners, and urban water engineers.

The development of tools to help cities and water utility authorities communicate and plan for long-term sustainable solutions is of utmost importance in the era of a changing and uncertain climate. This study introduces a hybrid modeling concept for the cosimulation of mesoscale blue-green stormwater systems and conventional urban sewer networks. The hybrid model successfully introduces the retention/detention effects of mesoscale blue-green stormwater systems to the hydraulic dynamics of the sewer network. The cosimulation package was further facilitated with a cost-oriented multiobjective optimization algorithm. The aim of the scalar multiobjective optimization was to minimize the total cost comprising both flooding costs and action costs – both parameters solely representing the financial components of cost – through optimal placement of mesoscale blue-green systems of optimal size. The suggested methodology provides a useful platform for sustainable management of the existing sewer networks in cities from a hydroeconomic perspective.

Complex chemical processes require complex simulation models. Selecting decision variables for optimization is increasingly difficult. This paper presents a study of a Subset Selection Algorithm (SSA) applied to the selection of decision variables to facili-tate a reduction of the decision variable combination sets to consider for a process designer, aimed towards improving said selection, optimization, and thereby resource efficiency. The results help conclude that SSA is able to reduce the consideration set of decision variable combinations for the process designer, and selects combination sets that are more effective in terms of minimizing the objective.