Modelling and Optimization of a Brewery Plant from Starch Sources using Aspen Plus

John Effiong Okon, I. Oboh
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Abstract

The brewing industry faces challenges with the use of malted barley as the primary starch source in Nigeria including quality control and standards, local production and demand, market competition, and price. The challenges of developing new product designs using pilot plants include inherent drawbacks and significant time constraints. This research explores the potential of sorghum as a valuable addition to barley in the brewing industry, especially in semi-arid regions like Africa. This study developed and optimised a model simulated using Aspen Plus for the brewing process, incorporating both malted barley and raw sorghum. Process parameters from a Brewery Plant formed the basis for the model to simulate the entire process from grain to fermented beverage. 1417.5Kg of raw sorghum grist was mashed in the mash copper vessel with 4800Kg of process water at 50℃ to observe protein rest and heated up to 93℃. The mash is cooled down to 75℃ via the mash cooler. At heating to 93℃ of the mash copper, 937.2kg of malted barley grist was mashed in the mash tun vessel with 3500Kg of process water at 50℃ to obverse protein rest. The cooled mash in the mash copper was transferred to the mash tun to achieve saccharification at 66℃ yielding 2460.658 kg/hr of wort after wort separation using a mash filter in the wort kettle. The wort was boiled to 100oC for concentration and sterilization. The boiled wort is pitched with brewer’s yeast (Saccharomyces cerevisiae) after chilling down to 9oC via a plate heat exchanger to commence fermentation. 1321.781 kg/hr of ethanol is produced in the fermentation storage tank during fermentation. Optimisation efforts focused on varying the barley to sorghum ratio, optimizing barley feed resulted in statistically significant improvements in ethanol yield (p<0.0001). The model's accuracy was confirmed through Box-behnken design and ANOVA, demonstrating strong agreement between actual and simulated ethanol yields. Additionally, pinch analysis facilitated heat exchanger optimization, enhancing energy efficiency and sustainability during the brewing process. Heat gained via the water side of the plate heat exchanger during wort cooling was charged into the system for sparging process amounting to energy cost saving of 0.34%. The economic analysis underscored the financial viability of the brewing process, with a total capital cost of $1,133,600.00 and annual operating expenses of $16,831,800.00. Raw material costs totaled $14,738,400.00 annually, while product sales generated $251,082,000.00 per year. Moreover, energy savings were achieved, with low pressure (LP) steam utilization saving $91 per year and refrigerant use contributing $46 annually. The desired rate of return for the project is set at 20% per year, with a payback period of 1.5 years. The findings from this study will contribute to the growing body of knowledge in the field of brewing process simulation and modeling and have practical implications for the brewing industry.
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使用 Aspen Plus 对来自淀粉源的啤酒厂进行建模和优化
在尼日利亚,酿酒业面临着使用麦芽作为主要淀粉来源的挑战,包括质量控制和标准、本地生产和需求、市场竞争和价格。利用试验工厂开发新产品设计所面临的挑战包括固有的缺点和严重的时间限制。本研究探讨了高粱作为大麦的重要添加物在酿酒业中的潜力,尤其是在非洲等半干旱地区。本研究开发并优化了一个使用 Aspen Plus 模拟酿造过程的模型,其中包含发芽大麦和原料高粱。酿酒厂的工艺参数是该模型模拟从谷物到发酵饮料整个过程的基础。将 1417.5 千克的生高粱谷物与 4800 千克的工艺用水在 50℃的温度下混合,以观察蛋白质的静止状态,然后加热至 93℃。醪液通过醪液冷却器冷却到 75℃。醪铜加热至 93℃时,937.2 千克麦芽浆在醪槽容器中与 3500 千克工艺水一起在 50℃下捣碎,以逆转蛋白质静止。在麦芽浆铜中冷却的麦芽浆被转移到麦芽浆调酒器中,在 66℃的温度下进行糖化,麦芽汁产量为 2460.658 千克/小时,在麦芽汁釜中使用麦芽浆过滤器进行麦芽汁分离。麦汁煮沸至 100 摄氏度进行浓缩和消毒。煮沸后的麦汁通过板式热交换器冷却至 9 摄氏度后,加入酿酒酵母(Saccharomyces cerevisiae)开始发酵。发酵过程中,发酵储存罐每小时产生 1321.781 公斤乙醇。优化工作的重点是改变大麦和高粱的比例,优化大麦喂料可显著提高乙醇产量(p<0.0001)。该模型的准确性通过方框-贝肯设计和方差分析得到了证实,表明实际乙醇产量与模拟乙醇产量之间非常一致。此外,捏合分析还有助于优化热交换器,提高酿造过程中的能源效率和可持续性。在麦汁冷却过程中,通过板式热交换器的水侧获得的热量被输入系统,用于喷洒过程,从而节省了 0.34% 的能源成本。经济分析强调了酿造工艺的财务可行性,总资本成本为 1,133,600.00 美元,年运营费用为 16,831,800.00 美元。原材料成本每年总计 14,738,400.00 美元,而产品销售额每年为 251,082,000.00 美元。此外,还实现了节能,利用低压蒸汽每年可节约 91 美元,使用制冷剂每年可节约 46 美元。该项目的预期回报率定为每年 20%,投资回收期为 1.5 年。这项研究的结果将为酿造工艺模拟和建模领域不断增长的知识做出贡献,并对酿造行业产生实际影响。
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