A collection of Aspen Plus files for various cases used in the study (see attached report). Simulation cases include a base case, an optimized base case, and variants including cellulose incineration and no decolorization step versions.

The accumulation of blended-fiber textile waste poses a significant environmental challenge. Conventional recycling processes are often unable to efficiently process textile blends due to their heterogeneity. Blended fibers are typically composed of a wide variety of synthetic and natural fibers, dyes, and coatings. The inability to separate and reuse valuable materials such as polyester, nylon, and cellulose fibers limits the potential for a sustainable circular economy. In this work, this challenge is addressed by designing and simulating a chemical recycling route for mixed textile waste using Aspen Plus®. The overall process concept is selected based on a systematic screening of existing valorization approaches and implemented based on results from a lab-scale study. In a glycolysis reaction, polyester is depolymerized into bis(2-hydroxyethyl) terephthalate (BHET), which is the main product of the process. Nylon and cotton fibers are retrieved as intact fibers. Selected process var... [more]

Aspen Plus models of an acetic acid production process from CO2 via electrolysis. See linked report for full details.

Electrochemical reduction of CO2 (eCO2RR) is a potential pathway for the defossilization of the process industry, offering the possibility of using captured carbon to produce a wide variety of green chemicals, such as CO, formic acid, acetic acid and ethanol, while simultaneously electrifying the chemical industry. However, liquid products from eCO2RR still face major challenges with respect to the downstream processing due to their high dilution in aqueous streams, the presence of electrolytes, and the formation of pinch points and azeotropes. This work investigates the integration of a CO2 electrolysis setup producing acetic acid at high selectivity with the downstream processing section. The process has been simulated in Aspen Plus with particular emphasis on the integration of electrolyzer waste heat via heat pumps and the recovery and recycling of electrolytes using established separation technologies. In contrast to commonly proposed approaches based on electrochemical separatio... [more]

Aspen Plus files that accompany the report Utilisation of Cement Flue Gas for Green Methanol Production: Process Design, Simulation, and Techno-Economic Assessment. See link.

This work valorises two industrial waste streams from Hope Cement Works—flue gas CO2 and low-grade waste heat—converting them into green methanol via catalytic hydrogenation with renewable hydrogen, while returning by-product O2 to the kiln for oxygen-enriched combustion. The plant captures 42.9 t/hr CO2 (88.7% efficiency) via monoethanolamine (MEA) absorption with rate-based RadFrac columns and produces 213 000 t/yr methanol over Cu/ZnO/Al2O3 at 230 ◦C/70 bar. Aspen Plus V14 simulation achieves 62.9% per-pass CO2 conversion and 99.7% overall via a 2.76:1 recycle loop. Six heat exchangers recover 66.5MW. Multi-objective ε-constraint optimisation reveals that the levelised cost of methanol (LCOM) and CO2 utilisation are positively correlated: the cost-optimal design achieves 99.5% utilisation because hydrogen (65–78% of operating expenditure, OPEX) is co-lost with CO2 in the purge. LCOM ranges from £779/t (£30/MWh) to £1303/t (UK grid); progressive integration of cement waste heat (£62/... [more]

The transition toward circular economy principles in water treatment requires advanced process systems engineering tools to evaluate the trade-offs between environmental sustainability and economic viability, particularly for energy-intensive Zero Liquid Discharge (ZLD) systems. While classic ZLD systems treat concentrated brine as waste, circular ZLD (CZLD) systems incorporate salt recovery technologies that generate marketable salt product. This study presents a comprehensive technoeconomic assessment framework for CZLD systems integrated with renewable energy. The framework is developed to evaluate different CZLD configurations that generate saleable sodium chloride. The assessment methodology integrates solar photovoltaic systems with increasing capacities (100-1400 kW) to analyze renewable energy penetration and energy storage requirements. The renewable energy integration model incorporates hierarchical energy dispatch algorithms prioritizing direct solar utilization, battery sto... [more]

The integrated recovery of heat and mechanical work has gained increasing importance in process integration due to the strong thermodynamic coupling between temperature and pressure changes in many industrial systems. This work presents a rigorous framework for the simultaneous synthesis of Work and Heat Exchanger Networks (WHEN), in which heating, cooling, compression, expansion, throttling, and pumping are optimized in a coordinated manner. The problem is formulated using Generalized Disjunctive Programming (GDP), allowing the explicit representation of alternative thermodynamic paths, phase-dependent behavior, and logical equipment choices. Process streams are defined by supply and target states, while only bounds are imposed on intermediate pressures, temperatures, and flow rates. Streams may change classification between hot and cold multiple times and may undergo several phase transitions.Rigorous thermodynamic correlations obtained from Aspen HYSYS are embedded in the optimizati... [more]

In the field of biochemical process design, the accurate modeling of microbial growth is essential for the development and optimization of biological reactors used in the production of high-value compounds. Achieving this objective requires a detailed understanding of how environmental factors-such as pH and nutrient availability-influence microbial dynamics across the four distinct growth phases: lag, exponential, stationary, and death. Traditionally, reactor design relies heavily on the Monod model, which provides a simplified representation of microbial growth, focusing primarily on the exponential phase under constant operating conditions (1). However, this model presents substantial limitations when applied to dynamic environments where key parameters vary over time. To overcome these constraints, the present study proposes a data-driven modeling approach using a multilayer perceptron (MLP) artificial neural network for the prediction of microbial growth trajectories under varying... [more]