Semantic function module pipeline generation

With modular automation, modular industrial plants use a functional engineering approach, and modules enable plug & produce plant engineering. However, plant configuration is still a largely manual process and often not optimized with respect to the available information. In this contribution, we propose a system and algorithm to support the automation engineer in the process of joining together modules into process pipelines and in their optimization. Our solution is built upon an abstract semantic data model that facilitates the automated matching of pre- and post-condition of modules and of the things that are processed by these modules. The pipeline generation engine is further extended by means of an optimization and ranking algorithm, and thus enables automated inter-module pipeline generation and plant optimization. We evaluate our system by means of a simple fictional use case scenario and prove feasibility, applicability as well as the huge potential for time and cost savings.

Exergy analysis and performance study for sour water stripper units, amine regenerator units and a Sulphur recovery unit of a refining plant

A refining column in the middle east that started its official production in 2020 provides its sour wastewater from all refinery plants to two sour water units (SWS1 and SWS2) to strip H2S and NH3. Sour gas from the refinery uses a lean amine solution for gas sweetening to absorb H2S in different absorbers. Rich amine with H2S is then stripped in two amine regeneration units (ARU1 and ARU2). The overhead of SWS and ARU units provide the acid gas feed to the sulphur recovery unit (SRU) to produce sulphur and prevent any acidic emissions against environmental regulations. First, the SWS1 unit is simulated using Aspen HYSYS V.11. A complete exergy study is conducted in the unit. Exergy destruction, exergy efficiency and percentage share in the destruction are calculated for all equipment. The highest exergy destruction rate was in the stripper with 5028.58 kW and a percentage share of 81.94% of the total destruction. A comparison was conducted between the exergy results of this study with two other exergy studies performed in the same refinery plant. The columns in the three studies showed the highest destruction rates exceeding 78% of the total destruction of each unit. The air coolers showed the second-highest destruction rates in their units with a percentage share exceeding 7% of the total destruction. The pumps showed the lowest destruction rates with values of less than 1% of the total destruction of each unit. Then, an individual simulation is conducted for stripper1 of SWS1, stripper2 for SWS2, regenerator1 of ARU1 and regenerator2 of ARU2. The individual simulations are combined in one simulation named combined simulation to compute the composition of acid gas from SWS and ARU units feeding SRU. Then, the SRU unit is simulated via a special package in HYSYS V.11 named SULSIM. The computed composition from SWS and ARU is exported to excel where it is linked with SRU simulation to calculate sulphur production. For the first time in any article in the world, all data feeding SWS, ARU, and SRU units are connected to a live system named Process Historian Database (PHD) to gather live data from the plant and perform plant optimization.

Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation, and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electromechanical hybrid electric vehicle (HEV) powertrain model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV powertrain model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.