Processing Cost Research Articles

Scalar Inferencing, Polarity and Cognitive Load

According to the Polarity Hypothesis, the presence or absence of a processing cost for Scalar Inferences (SIs) depends on their polarity. This hypothesis predicts, among other things, that the processing of lower-bounding SIs should not be affected by cognitive load the same way upper-bounding SIs are. To date, evidence in support of this prediction comes from the comparison between upper-bounding and lower-bounding SIs elicited by disparate scalar words. In this paper, we report on two dual-task experiments testing this prediction in a more controlled way by comparing upper-bounding and lower-bounding SIs arising from the same scalar words or scale-mates operating over the same dimension. Results show that, for these more minimal comparisons, lower-bounding SIs involve comparable cognitive demands as their upper-bounding counterparts. These findings challenge the idea that load effects are consistently modulated by SI polarity and suggest instead that these effects are relatively consistent across different types of SIs.

GREEN SYNTHESIS, CHARACTERIZATION AND PHOTOCATALYTIC ACTIVITY OF NEW PHOTOCATALYST SrCo2O4 UNDER VISIBLE LIGHT IRRADIATION

In order to reduce the enormous costs of photocatalytic processes, the development of new photocatalysts sensitive to visible light constitutes a promising strategy to boost the efficiency of this method in water treatment. In this paper, strontium cobaltite nanoparticles (SrCo2O4 NPs) were shaped by simple, ecological and economical methods using cobalt and strontium nitrates as precursor and freshly isolated chicken egg white as capping agents. The crystalline product SrCo2O4 NPs was characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and Ultraviolet–Visible (UV–Vis) spectroscopy studies. The SrCo2O4-catalyzed Congo red (CR) degradation under visible light is investigated. XRD analysis showed that the sample was indeed crystallized in the cubic spinel structure (space group Fd3m). The average size of the nanoparticles was estimated to be ∼28[Formula: see text]nm. The FT-IR spectrum shows two bands at 620 and 573[Formula: see text][Formula: see text], which are characteristic of the spinel strontium cobaltite crystalline structure. The two optical band gap energy of synthesized photocatalyst estimated from UV–Visible spectrum is 2.07 and 3.48[Formula: see text]eV. The developed photocatalyst exhibits significant photocatalytic degradation of RC in an acidic medium with 97% of the dye mineralized after 5[Formula: see text]h.

Investigating the efficiency of electrocoagulation using similar/dissimilar electrodes for the detoxification of Coralene Rubine dye: a cost effective approach.

Efficient treatment of textile dyeing wastewater can be achieved through electrocoagulation (EC) with minimal sludge production; however, the selection of the appropriate electrode is essential in lowering overall costs. Also, the reuse of the treated aqueous azo dye solution from this process has not been explored in detail. With these objectives, this study aims to treat synthetic azo dye solutions and achieve high colour removal efficiency (CRE%) using similar (Ti-Ti) and dissimilar (Ti-Cu) metal electrodes through EC with an attempt to reduce the cost. The aqueous Coralene Rubine GFL azo dye was used to examine the efficiency and cost of the EC process. X-Ray Photoelectron Spectroscopy was used to study the EC mechanism, while High Performance Liquid Chromatography was used to analyse the degradation of the dye and the formation of intermediate compounds. The concentration of metal ions in the treated dye solution was quantified using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), with Ti-Ti treated solution having 14.20mg/L concentration of Ti and Ti-Cu treated solution having 0.078mg/L of Ti and 0.001mg/L of Cu, respectively. Colour removal efficiency of 99.49% was obtained for both electrode sets, with a lower operating time and voltage for dissimilar metal combination. Ecotoxicity studies showed negligible toxicity of Ti-Cu treated dye samples compared to untreated solutions. Survival rate, protein estimation, and catalase activity was used to validate the treatment method's efficacy. The study found that the dissimilar electrode material exhibited reduced toxicity due to the presence of heavy metals below the permissible limit.

Post-processing of phase change material in a zero-change commercial silicon photonic process

Integration of phase change material (PCM) with photonic integrated circuits can transform large-scale photonic systems by providing non-volatile control over phase and amplitude. The next generation of commercial silicon photonic processes can benefit from the addition of PCM to enable ultra-low power, highly reconfigurable, and compact photonic integrated circuits for large-scale applications. Despite all the advantages of PCM-based photonics, today’s commercial foundries do not provide them in their silicon photonic processes yet. We demonstrate the first-ever electrically programmable PCM device that is monolithically post-processed in a commercial foundry silicon photonics process using a few fabrication steps and coarse-resolution photolithography. These devices achieved 1.4 dB/μm of amplitude switching contrast using a thin layer of 12.5 nm GeSbTe in this work. We have also characterized the reconfiguration speed as well as repeatability of these devices over 20,000 switching cycles. Our solution enables non-volatile photonic VLSI systems that can be fabricated at low cost and high reliability in a commercial foundry process, paving the way for the development of non-volatile programmable photonic integrated circuits for a variety of emerging applications.

Modeling and validation of multi-objective optimization for mixed xylene hybrid distillation/crystallization process

Innovative hybrid distillation/crystallization processes offer significant energy saving and cost reduction for the separation of isomer mixtures. Despite of the enormous potential of hybrid separation processes, they are not widely exploited in industrial applications due to the complexity of the design and multi-objective optimization of these highly integrated processes. In this study, the mixed xylene hybrid scheme was first simulated to provide the initial value of non-dominated sorted genetic algorithm-III. The operating parameters were then optimized through the Python-Aspen platform. Coupled with the online monitoring tool, a rigorous melt crystallization experiment was developed to track crystal size distribution (CSD) and crystal shape. By comparing the optimized results with the original ones, the total annual cost and CO2 emissions were, respectively, reduced by 5.35 % and 12.80 %. Through the experimental validation, the accuracy of the simulation-based crystallization CSD prediction method was discussed, and the potential and challenges of this novel method were proposed. This work aims to minimize the total annual cost and CO2 emissions of the mixed xylene hybrid process simultaneously and explore the feasibility of CSD prediction method by extracting two-dimensional crystal size information from online microscopic images.

A fragment of the β-glucosidase gene from the rumen fungus Neocallimastix patriciarum J11 encodes a recombinant protein that exhibits activities in β-glucosidase and β-glucanase

Lignocellulose, the most abundant organic waste on Earth, is of economic value because it can be converted into biofuels like ethanol by enzymes such as β-glucosidase. This study involved cloning a β-glucosidase gene named JBG from the rumen fungus Neocallimastix patriciarum J11. When expressed recombinantly in Escherichia coli, the rJBG enzyme exhibited significant activity, hydrolyzing 4-nitrophenyl-β-d-glucopyranoside and cellobiose to release glucose. Surprisingly, the rJBG enzyme also showed hydrolytic activity against β-glucan, breaking it down into glucose, indicating that the rJBG enzyme possesses both β-glucosidase and β-glucanase activities, a characteristic rarely found in β-glucosidases. When the JBG gene was expressed in Saccharomyces cerevisiae and the transformants were inoculated into a medium containing β-glucan as the sole carbon source, the ethanol concentration in the culture medium increased from 0.17 g/L on the first day to 0.77 g/L on the third day, reaching 1.3 g/L on the fifth day, whereas no ethanol was detected in the yeast transformants containing the recombinant plasmid pYES-Sur under the same conditions. These results demonstrate that yeast transformants carrying the JBG gene can directly saccharify β-glucan and ferment it to produce ethanol. This gene, with its dual β-glucosidase and β-glucanase activities, simplifies and reduces the cost of the typical process of converting lignocellulose into bioethanol using enzymes and yeast.

Surfactant-Mediated Microalgal Flocculation: Process Efficiency and Kinetic Modelling.

Microalgae are a valuable source of lipids, proteins, and pigments, but there are challenges in large-scale production, especially in harvesting. Existing methods lack proven efficacy and cost-effectiveness. However, flocculation, an energy-efficient technique, is emerging as a promising solution. Integrating surfactants enhances microalgal harvesting and disruption simultaneously, reducing processing costs. This study investigated cetyltrimethylammonium bromide (CTAB), dodecyltrimethylammonium bromide (DTAB), and sodium dodecyl sulphate (SDS) for harvesting Tetraselmis sp. strains (75LG and 46NLG). CTAB exhibits superior results, with 88% harvesting efficiency at 1500 and 2000 mg L-1 for 75LG and 46NLG, respectively, for 60 min of sedimentation-thus being able to reduce the operating time. Beyond evaluating harvesting efficiency, our study explored the kinetics of the process; the modified Gompertz model led to the best fit. Furthermore, the largest kinetic constants were observed with CTAB, thus highlighting its efficacy in optimising the microalgal harvesting process. With the incorporation of the suggested enhancements, which should be addressed in future work, CTAB could hold the potential to optimise microalgal harvesting for cost-effective and sustainable large-scale production, eventually unlocking the commercial potential of microalgae for biodiesel production.

Production and SERS characterization of bacteriocin-like inhibitory substances by latilactobacillus sakei in whey permeate powder: exploring natural antibacterial potential.

Bacteriocins are antimicrobial compounds that have awakened interest across several industries due to their effectiveness. However, their large-scale production often becomes unfeasible on an industrial scale, primarily because of high process costs. Addressing this challenge, this work analyzes the potential of using low-cost whey permeate powder, without any supplementation, to produce bacteriocin-like inhibitory substances (BLIS) through the fermentation of Latilactobacillus sakei. For this purpose, different concentrations of whey permeate powder (55.15gL-1, 41.3gL-1 and 27.5gL-1) were used. The ability of L. sakei to produce BLIS was evaluated, as well as the potential of crude cell-free supernatant to act as a preservative. Raman spectroscopy and surface-enhanced Raman scattering (SERS) provided detailed insights into the composition and changes occurring during fermentation. SERS, in particular, enhanced peak definition significantly, allowing for the identification of key components, such as lactose, proteins, and phenylalanine, which are crucial in understanding the fermentation process and BLIS characteristics. The results revealed that the concentration of 55.15gL-1 of whey permeate powder, in flasks without agitation and a culture temperature of 32.5°C, presented the highest biological activity of BLIS, reaching 99% of inhibition of Escherichia coli and Staphylococcus aureus with minimum inhibitory concentration of 36-45%, respectively. BLIS production began within 60h of cultivation and was associated with class II bacteriocins. The results demonstrate a promising approach for producing BLIS in an economical and environmentally sustainable manner, with potential implications for various industries.

Efficient Bio-Oxidation of Cellobiose with Engineered Gluconobacter oxydans to Provide Highly Concentrated Cellobionic Acid

Cellobionic acid (CBA) can be obtained through the oxidation of cellobiose, the monomer of cellulose. CBA serves as a plant-based alternative to its stereoisomer lactobionic acid, which is used in the pharmaceutical, cosmetic, and food industries. Gluconobacter oxydans is a well-established whole-cell biocatalyst with membrane-bound dehydrogenases (mDH) for regio-specific oxidations. As G. oxydans wildtype cells show low cellobiose oxidation activities, the glucose mDH from Pseudomonas taetrolens was overexpressed in G. oxydans BP9, a multi mDH deletion strain. Whole-cell biotransformation studies were performed with resting cells of the engineered G. oxydans in stirred tank bioreactors. Initial biomass specific cellobionate formation rates increased with increasing cellobiose concentrations up to 190 g L−1, and were constant until the solubility limit. The maximal volumetric CBA formation rates and the oxygen uptake rates increased linearly with the concentration of engineered G. oxydans. This enables the estimation of the maximum biocatalyst concentration limited by the maximum oxygen transfer rate of any bioreactor. Thus, 5.2 g L−1 G. oxydans was sufficient to produce 502 g L−1 CBA with >99% yield in a simple aerobic batch process. The highly concentrated CBA will reduce downstream processing costs considerably after cell separation.

Analysis Of The Added Value Of Processing Coffee Beans Into Packaged Coffee Powder

The agricultural sector is the main pillar of Indonesia’s economic development. Coffee is a crop that plays an important role because its demand continues to increase. Processing coffee beans (green beans) into packaged coffee powder is one effort to increase added value so that it has the potential to increase farmers’ income. Research aims to determine the processing process, costs incurred, receipts, revenue, and profits, As well as to determine the added value obtained from the process of processing coffee beans into packaged coffee powder of the Berkah Kopi Tapin brand managed by the Baru Muncul Farmers Group in Asam Randah Village, Hatungun district, carried out from April to May 2024. The research data is primary data obtained from observations and interviews with the heads of the farmers and labor groups who handle the coffee processing process. Secondary data was obtained from literature and previous research. Data analysis used the theory of cost, revenue, income, and profit and used the Hayami method to calculate added value. The results showed that processing 50 kg of robusta coffee green bean produced 400 packages with a price of IDR 15,000 per 100 grams. Total costs amounted to IDR 2,882,214, revenue amounted to IDR 6,000,000, and income amounted to IDR 3,117,786. profit amounted to IDR 3,117,786. The added value obtained was IDR 70,156 per kg of raw material with a value-added ratio of 58%. Labor income of IDR 7,800 per kg of raw material means that the contribution of labor is 10%. The profit level is IDR 62,356 per kg. the margin obtained from the analysis of the added value of coffee bean processing is IDR 80,000 per kg of raw material. The amount of margin on production factors is 9.15% for labor compensation. 12.3% for other input contributions. And 77.94% for the profits of the Baru Muncul Farmer Group.

Construction and optimization of a waste heat and off-peak operation-assisted absorption carbon capture system

ABSTRACT Carbon capture and storage can reduce the carbon emission directly and effectively. Absorption carbon capture (ACC) is currently the most promising way to remove CO2 from coal-fired power plant, yet the main drawback is the excessive thermal energy consumption for the regeneration process. In this study, a new ACC system that utilizing the low-grade waste heat to generate steam has been proposed, in which system the steam is compressed to increase the saturation temperature to satisfy the thermal energy grade demand of the absorbent regeneration. Heat pump is introduced to increase the inlet pressure and enhance the energy and economic effect of vapor compression and R245fa is found suitable for the system. The vapor generating work can be controlled below 200 kWh t−1 when T wh is higher than 60°C, and COPvg can be higher than 3.5 for situations of T tag,sat not exceeding 130°C. The energy performance and economic analysis of the proposed ACC system driven by the new vapor generation method is analyzed. Off-peak operation by ASS shows good economic effect, the operating cost for the regeneration process of carbon capture is only 69.70 CNY t−1 CO2 under the worst condition and far below the traditional heating method. The new heat supply method for carbon capture presents reasonable energy and economic performance, which is shown suitable for peak shifting of the power grid.

Rational design of melamine-crosslinked poly(ethylene glycol) membranes for sour gas purification

Natural gas accounts for about a quarter of the world's energy consumption; however, most of the current natural gas reservoirs are sour. Sour natural gas containing significant amounts of hydrogen sulfide (H2S) along with carbon dioxide (CO2) must be purified to meet the strict requirements for transportation and storage in the industry. Sour natural gas purification involves two steps: acid gas removal (AGR) and acid gas enrichment (AGE), which currently use highly energy-intensive technologies. Membrane separation represents the most attractive technology to lower the operating cost and carbon footprint of these large processes. Guided by density functional theory (DFT) calculations, we rationally designed a crosslinked poly(ethylene glycol) (PEG) membrane for AGR and AGE applications. Specifically, PEG-bisazide monomers were crosslinked with a novel trialkyne-functionalized H2S-philic melamine via azide-alkyne cycloaddition click chemistry. The developed membranes were characterized and tested for (H2S + CO2)/CH4 and H2S/CO2 separations under realistic industrial conditions, targeting the AGR and AGE applications, respectively. These novel membranes achieved high H2S permeability while maintaining attractive H2S/CO2 selectivity.

Process design, performance comparison and operating test of the multi-stage vertical absorption heat exchanger realizing independent heating in three zones

Absorption heat exchangers applied in district heating systems can significantly reduce the outlet water temperature of the primary network, thereby expanding the heating scale and promoting low-temperature waste heat recovery. However, nearly 50% of the existing heating systems in high-rise buildings are divided into three independent zones. At present, there is a lack of research for absorption heat exchangers in this scenario. The main task of this paper is to address this issue to expand the application range of absorption heat exchangers. Firstly, two different processes are designed in this paper. The first process is to add a plate heat exchanger in the existing two-stage process, while the second process is a novel three-stage vertical process, consisting of three absorption heat pumps with different pressures and three sets of plate heat exchangers. Then, the operating performance, self-adjustment ability and the system cost of different processes are compared based on the simulation method. Although the price of Process 2 is 26.4% higher than that of Process 1, the efficiency of Process 2 is 20.3%-27.7% higher, and the self-regulation ability is significantly better. Thus, Process 2 has better overall performance. Moreover, the system development and operating test of Process 2 are conducted. Throughout the whole heating season, the overall efficiency of the system is 1.17–1.23. Without manual intervention, the relative standard deviations of the inlet water temperature of the secondary network and the actual heat supply rate ratio are less than 5% and 16% respectively, which proves that the system has great self-adjustment ability. The conclusions in this paper can guide the design and application of the absorption heat exchanger in three-zone independent heating scenarios.

Real-time scheduling of power grid digital twin tasks in cloud via deep reinforcement learning

As energy demand continues to grow, it is crucial to integrate advanced technologies into power grids for better reliability and efficiency. Digital Twin (DT) technology plays a key role in this by using data to monitor and predict real-time operations, significantly enhancing system efficiency. However, as the power grid expands and digitization accelerates, the data generated by the grid and the DT system grows exponentially. Effectively handling this massive data is crucial for leveraging DT technology. Traditional local computing faces challenges such as limited hardware resources and slow processing speeds. A viable solution is to offload tasks to the cloud, utilizing its powerful computational capabilities to support the stable operation of the power grid. To address the need, we propose GD-DRL, a task scheduling method based on Deep Reinforcement Learning (DRL). GD-DRL considers the characteristics of computational tasks from the power grid and DT system and uses a DRL agent to schedule tasks in real-time across different computing nodes, optimizing for processing time and cost. We evaluate our method against several established real-time scheduling techniques, including Deep Q-Network (DQN). Our experimental results show that the GD-DRL method outperforms existing strategies by reducing response time, lowering costs, and increasing success rates.

Merger of internet of things and machine learning: The internet of everything sector projects, benefits, and future roles

The convergence of the Internet of Things and Machine Learning shapes key developments in information technologies, such as the Internet of Everything environment. The Internet of Things' Machine-to-Machine connectivity advances, real-time Big Data storage, and data-driven Machine Learning synergize the scope of Internet of Everything innovations. As the Internet of Things and Machine Learning merger continues, the Internet of Everything reshapes the world sector map by providing unprecedented possibilities and efficiencies. The propelled Internet of Everything is ushering in a new era of transformative projects in various sectors such as Healthcare and Life Science, Manufacturing, Agriculture, Energy, Transportation, and Construction. The high-tech transformative projects offer many applications in process optimization, cost reduction, increased productivity, and efficiency. Our work examines the prominent joint Internet of Things and Machine Learning initiatives, projects and works to encourage new initiatives and multidisciplinary work worldwide. We also discuss the benefits of transformative projects in different sectors in detail and put forward a forecast for their future roles. Our study is supported by solid academic literature and project examples.

Electrochemical deposition combined with high-temperature molecular sieve adsorption strategy: Efficient removal of key fission element yttrium for purification of radioactive molten salts

In order to realize energy cost savings in waste salt recycling and electrolysis processes during the nuclear fuel cycle, this paper presents a novel approach for the purification of waste molten salt by employing molecular sieves to absorb excess fission products selectively. The method is founded on the principles of electrolytic refining to achieve sustainable development of reprocessing. In this study, a systematic investigation of the electrochemical reduction/oxidation processes of Y(III)/In(III) was performed on electrode, as well as the electrode reaction of Y(III) on an In electrode at 773 K. Constant-current electrolysis produced an extraction rate of 93.11 % for Y(III) on the In electrode. In order to improve the current efficiency, greatly save the processing cost, and realize the sustainable development of dry post-processing. Subsequently, the waste molten salt environment following electrolysis was simulated, and the fission product was effectively adsorbed utilizing 5A molecular sieves, resulting in a yttrium removal rate of up to 99.94 %. This process not only significantly reduces the cost of electrolysis but also enhances the removal efficiency of target nuclides. The study examined the molten salt adsorption mechanism of molecular sieve, determining the primary adsorption mechanism to be chemical one (ion exchange), through the application of pseudo-first-order, pseudo-second-order, and Weber-Morris kinetic models for re-evaluation.

Efficient neural implicit representation for 3D human reconstruction

High-fidelity digital human representations are increasingly in demand in the digital world, particularly for interactive telepresence, AR/VR, 3D graphics, and the rapidly evolving metaverse. Even though they work well in small spaces, conventional methods for reconstructing 3D human motion frequently require the use of expensive hardware and have high processing costs. This study presents HumanAvatar, an innovative approach that efficiently reconstructs precise human avatars from monocular video sources. At the core of our methodology, we integrate the pre-trained HuMoR, a model celebrated for its proficiency in human motion estimation. This is adeptly fused with the cutting-edge neural radiance field technology, Instant-NGP, and the state-of-the-art articulated model, Fast-SNARF, to enhance the reconstruction fidelity and speed. By combining these two technologies, a system is created that can render quickly and effectively while also providing estimation of human pose parameters that are unmatched in accuracy. We have enhanced our system with an advanced posture-sensitive space reduction technique, which optimally balances rendering quality with computational efficiency. In our detailed experimental analysis using both artificial and real-world monocular videos, we establish the advanced performance of our approach. HumanAvatar consistently equals or surpasses contemporary leading-edge reconstruction techniques in quality. Furthermore, it achieves these complex reconstructions in minutes, a fraction of the time typically required by existing methods. Our models achieve a training speed that is 110× faster than that of State-of-The-Art (SoTA) NeRF-based models. Our technique performs noticeably better than SoTA dynamic human NeRF methods if given an identical runtime limit. HumanAvatar can provide effective visuals after only 30 s of training. Please visit https://github.com/HZXu-526/Human-Avatar for further demo results and code.

Polarization-controlled metasurface design based on deep ResNet

The accuracy of metasurface inverse design depends on the input dimension of the target light field and the output dimension of the unit structure parameters. When the two light field targets are simultaneously controlled, the designed unit structure often cannot satisfy the simultaneous directional control of the two light field target parameters due to the existence of coupling. This greatly limits the completeness of the function of metasurfaces in applications. In this paper, we apply the designed 13-layer residual network (ResNet) to the inverse design of wavefront control of dielectric metasurfaces. After the neural network training is completed, the correspondence between any amplitude, phase and the element structure parameters within a certain error range can be realized, which greatly saves the time cost in the metasurface design process. As a verification of the inverse design method, we arranged two focusing metasurfaces of different sizes. Their focusing efficiencies are more than 53%. The nanounit structure and its inverse design method proposed in this paper provide a new idea for the design of the metasurface to control the polarized light field. At the same time, it will have important applications in the intelligent design of metasurfaces.

Dehazing & Reasoning YOLO: Prior knowledge-guided network for object detection in foggy weather

Fast and accurate object detection in foggy weather is crucial for visual tasks such as autonomous driving and video surveillance. Existing methods typically preprocess images with enhancement techniques before the object detector, so that the real-time performance of object detection decreases to some extent. Meanwhile, many popular object detection models rely solely on visual features for localization and classification. When fog is present, visual features would be so adversely impacted that the detection accuracy sharply decreases. Therefore, we propose an end-to-end prior knowledge-guided network called DR-YOLO for object detection in foggy weather. DR-YOLO integrates the atmospheric scattering model and the co-occurrence relation graph as prior knowledge into the entire training process of the detector. Firstly, Restoration Subnet Module (RSM) is designed to employ the atmospheric scattering model to guide the learning direction of the detector for dehazing features. Specifically, it is only adopted during the training process and does not increase the time cost of detection process. Secondly, for guiding the detector to pay more attention to potential co-occurring objects in the same scene, we introduce Relation Reasoning Attention Module (RRAM) that utilizes the co-occurrence relation graph to supplement deficient visual features in foggy weather. In addition, DR-YOLO employs Adaptive Feature Fusion Module (AFFM) to effectively merge the key features from the backbone and neck for the needs of RRAM and RSM. Finally, we conduct experiments on clear, synthetic and real-world foggy datasets to demonstrate the effectiveness of DR-YOLO. The source code is available at https://github.com/wenxinss/DR-YOLO.

A techno-economic assessment of conventional and modified Solvay processes for CO2 capture and reject brine desalination

The ongoing discharge of desalination reject brine and emission of carbon dioxide (CO2) into the environment pose a major threat to the ecosystem. In this context, the Solvay process presents a potential mitigation scheme for reducing reject brine salinity while simultaneously sequestering CO2. This work reports for the first time a systematic techno-economic assessment of the conventional (ammonia-based) and modified (calcium hydroxide-based and Potassium hydroxide-based) Solvay processes. The model evaluates the effect of reactor conversion, inlet CO2 weight%, inlet CO2 temperature, gas/liquid ratio, and brine flow rate on the CO2 and Sodium ions removal. In addition, the process cost associated with each parameter was analyzed, and the impact of implementing carbon tax on process profitability was investigated. Furthermore, the Solvay processes were evaluated using economic metrics including Net Present Value (NPV), economy of scale, and Levelized cost of water. The results suggest that the calcium hydroxide-based modified Solvay process outperforms other processes in terms of CO2 and Sodium ions removal. Moreover, for the studied parameters, the calcium hydroxide-based modified Solvay process demonstrated economic viability, yielding a decent annual profit. However, the conventional Solvay process requires a remarkable amount of expenses, yet after the implementation of 40 dollars per metric tons of CO2 as a carbon tax, the conventional Solvay reaches the break-even point. These findings underscore the importance of implementing environmentally friendly practices in the industry, as the modified Solvay process proves to be a promising solution for mitigating environmental threats and promoting sustainable operations.