CN101872374A - A method for optimizing beer production formula - Google Patents

A method for optimizing beer production formula Download PDF

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CN101872374A
CN101872374A CN 201010180631 CN201010180631A CN101872374A CN 101872374 A CN101872374 A CN 101872374A CN 201010180631 CN201010180631 CN 201010180631 CN 201010180631 A CN201010180631 A CN 201010180631A CN 101872374 A CN101872374 A CN 101872374A
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葛铭
郑松
魏江
郑小青
李春富
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Hangzhou Kuntian Automation System Co ltd
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Hangzhou Dianzi University
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Abstract

The invention relates to a method for optimizing a beer production formula. A conventional production formula is determined by a method which combines human experience and tests. The method comprises the following steps of: obtaining main raw materials in a beer formula; establishing a comprehensive production performance index estimation model according to the parameters of the raw materials, wherein main considered comprehensive performance indexes comprise saccharifying power, wort total nitrogen, wort alpha amino nitrogen, wort beta glucan and the thickness of a distilled grain layer of a filter tank; and optimizing a beer formula model by using a variable-metric ant colony optimization method and finally obtaining a production formula with the lowest cost. The method of the invention has the advantages of openness, robustness, parallelism and global convergence.

Description

一种啤酒生产配方优化方法 A method for optimizing beer production formula

技术领域technical field

本发明属于信息与控制技术领域，涉及自动化技术，特别是涉及一种啤酒生产配方优化方法。The invention belongs to the field of information and control technology, relates to automation technology, in particular to a method for optimizing beer production formula.

背景技术Background technique

原料配方优化设计问题是化工、食品、材料等领域中的一个重要研究内容。为了获得性能优异、能满足使用要求的配方，需根据产品的性能要求和工艺条件，通过试验、优化、鉴定、合理地选用原材料，确定各种原材料的用量配比关系。近年来对配方优化设计的应用研究开始活跃，对于这样一个复杂的多目标配方体系，目前大多采用试验设计的方法。啤酒企业配方设计也不例外，为了酿造啤酒风格的一致，麦芽进厂后通过分析，进行原料组分的概算，决定配料方案，再通过小型糖化检查、验证，最后定出配料生产试验方案。通过生产验证修改，最终订出产品配料。The optimal design of raw material formula is an important research content in the fields of chemical industry, food and materials. In order to obtain a formula with excellent performance that can meet the requirements of use, it is necessary to determine the dosage ratio of various raw materials through testing, optimization, identification, and rational selection of raw materials according to the performance requirements and process conditions of the product. In recent years, the research on the application of formulation optimization design has become active. For such a complex multi-objective formulation system, most of them adopt the method of experimental design. The formula design of beer enterprises is no exception. In order to keep the style of brewing beer consistent, the malt is analyzed after entering the factory, the raw material components are estimated, and the ingredient plan is determined. After small-scale saccharification inspection and verification, the ingredient production test plan is finally determined. Through production verification and modification, the product ingredients are finally ordered.

在啤酒生产过程中，啤酒原料配方决定了产品的风味、质量、成本等重要指标。目前，啤酒生产企业生产配方仍然依靠人工经验结合试验的方法来确定，虽然可以满足工艺的基本要求，但是原料总成本、口味等指标参数容易偏高。因此对啤酒配方进行优化对提高企业生产效率、降低产品成本具有重要意义。啤酒配方优化是一个含多极值点的配方优化问题。迄今为止，配方优化问题多采用常见的传统数学优化方法，如单纯形法、共轭梯度法、几何平均分析法、正交设计法等。由于这些优化方法缺乏全局最优搜索的鲁棒性，且绝大多数传统优化都需要梯度信息，因而要求解此类具有复杂数学形式的优化问题，相当困难。In the process of beer production, the formula of beer raw materials determines the important indicators such as the flavor, quality and cost of the product. At present, the production formula of beer production enterprises is still determined by manual experience combined with experiments. Although it can meet the basic requirements of the process, the total cost of raw materials, taste and other index parameters are likely to be too high. Therefore, it is of great significance to optimize the beer formula to improve the production efficiency of the enterprise and reduce the product cost. Beer recipe optimization is a recipe optimization problem with multiple extreme points. So far, common traditional mathematical optimization methods have been used for formula optimization problems, such as simplex method, conjugate gradient method, geometric mean analysis method, orthogonal design method, etc. Since these optimization methods lack the robustness of global optimal search, and most traditional optimization requires gradient information, it is quite difficult to solve such optimization problems with complex mathematical forms.

发明内容Contents of the invention

本发明的目标是针对啤酒配方优化中的一些难题，提出一种具有较强全局优化能力的配方优化方法。The object of the present invention is to propose a formula optimization method with strong global optimization ability aiming at some difficult problems in beer formula optimization.

本发明的技术方案是将连续的配方优化问题转变成离散的组合优化问题，然后采用仿生的蚁群算法，并采用变尺度的方法改善算法寻优性能，缩短计算时间，最终确立了一种啤酒生产配方优化方法。The technical solution of the present invention is to transform the continuous formula optimization problem into a discrete combinatorial optimization problem, then adopt the bionic ant colony algorithm, and adopt the method of variable scale to improve the optimization performance of the algorithm, shorten the calculation time, and finally establish a beer Production recipe optimization method.

本发明方法的具体步骤是：The concrete steps of the inventive method are:

步骤1、获取啤酒配方中主要原料(包括大麦麦芽、特种麦芽、小麦麦芽和辅助原料等)的产槽率、α氨基氮、糖化力、总可溶性氮、β葡聚糖以及单价等参数，这些数据可以通过供应商获得，也可通过生产过程中统计获取；Step 1. Obtain parameters such as tank production rate, α-amino nitrogen, saccharification power, total soluble nitrogen, β-glucan, and unit price of the main raw materials (including barley malt, special malt, wheat malt, and auxiliary raw materials) in the beer formula. The data can be obtained through the supplier, or through statistics during the production process;

步骤2、通过原料的参数建立综合生产性能指标估算模型，考虑的主要综合性能指标为麦芽糖化力、麦汁总氮、麦汁α氨基氮、麦汁β葡聚糖以及过滤槽糟层厚度。Step 2. Establish a comprehensive production performance index estimation model based on raw material parameters. The main comprehensive performance indexes considered are maltosaccharification power, wort total nitrogen, wort α-amino nitrogen, wort β-glucan, and lauter tank dross layer thickness.

①麦芽糖化力① Maltosaccharification power

在正常糖化操作下(65～68℃糖化30～45min)，每千克混合原料投料中，应含有1500～2000WK的糖化力。上限值可以缩短精化时间，并得到较高发酵度的麦汁；下限值糖化时间长，发酵度低。如小于1500WK会影响糖化作业、影响原料利用率、影响麦汁组成。且麦芽的总可溶性氮可按下式进行估算：Under normal saccharification operation (saccharification at 65-68°C for 30-45 minutes), the saccharification power of 1500-2000WK should be contained in every kilogram of mixed raw materials fed. The upper limit can shorten the refining time and obtain wort with a higher degree of fermentation; the lower limit can increase the saccharification time and lower the degree of fermentation. If it is less than 1500WK, it will affect the saccharification operation, affect the utilization rate of raw materials, and affect the composition of wort. And the total soluble nitrogen of malt can be estimated as follows:

Figure GSA00000134752600021
式中，Ti、Xi分别为组分i的糖化力强度及质量分数，1≤i≤n；
Figure GSA00000134752600021
In the formula, T i and X i are the saccharification force intensity and mass fraction of component i respectively, 1≤i≤n;

②麦汁总氮②Wort total nitrogen

通常啤酒定型麦汁的总可溶性氮水平为：Usually the total soluble nitrogen level of beer finalized wort is:

全麦芽麦汁              900～1200mg/LWhole malt wort 900～1200mg/L

加辅料浓醇型啤酒麦汁    700～850mg/LConcentrated beer wort with auxiliary materials 700～850mg/L

加辅料淡爽型啤酒麦汁    550～700mg/LLight and refreshing beer wort with auxiliary materials 550～700mg/L

并且，麦汁的总可溶性氮可按下式进行估算：And, the total soluble nitrogen of wort can be estimated as follows:

Figure GSA00000134752600022
式中，
Figure GSA00000134752600023
为组分i的总氮质量浓度；A、V分别为一批次需要的原料总质量和要求的麦汁产量，单位分别为千克以及升；γ、
Figure GSA00000134752600024
分别为蛋白质分解强度和煮沸氮析出率。
Figure GSA00000134752600022
In the formula,
Figure GSA00000134752600023
is the total nitrogen mass concentration of component i; A and V are the total mass of raw materials required for a batch and the required wort output, respectively, in kilograms and liters; γ,
Figure GSA00000134752600024
Respectively, protein decomposition intensity and boiling nitrogen precipitation rate.

③麦汁α氨基氮③Wort α amino nitrogen

麦汁α氨基氮均与啤酒的风味物质高级醇和双乙酰相关，控制麦汁α氨基氮浓度对于控制啤酒风味尤为重要。一般麦汁α氨基氮控制在160～180mg/L范围内比较适宜。且麦汁α氨基氮含量可以通过下式估算：The α-amino nitrogen in wort is related to higher alcohols and diacetyl in beer, and controlling the concentration of α-amino nitrogen in wort is particularly important for controlling beer flavor. Generally, it is more appropriate to control wort α-amino nitrogen in the range of 160-180mg/L. And the wort α-amino nitrogen content can be estimated by the following formula:

式中，

Figure GSA00000134752600026
为组分i的α氨基氮质量浓度，κ为氨基氮系数。 In the formula,
Figure GSA00000134752600026
is the α-amino nitrogen mass concentration of component i, and κ is the amino-nitrogen coefficient.

④麦汁β葡聚糖④Wort β-glucan

啤酒中含有适量的β葡聚糖是保持啤酒具有醇厚感的物质之一，但是如果过多的β葡聚糖会在制麦中分解不足，在啤酒酿造过程中会带来一系列相关的问题。因此在制麦过程β葡聚糖的含量低于250mg/L。且麦汁β葡聚糖含量可以通过下式估算：An appropriate amount of β-glucan in beer is one of the substances to maintain the mellow taste of beer, but if too much β-glucan will not be decomposed enough during malting, it will cause a series of related problems in the beer brewing process . Therefore, the content of β-glucan in the malting process is lower than 250mg/L. And the wort β-glucan content can be estimated by the following formula:

Figure GSA00000134752600031
式中，为组分i的β葡聚糖质量浓度，ν为葡聚糖系数。
Figure GSA00000134752600031
In the formula, is the mass concentration of β-glucan in component i, and ν is the glucan coefficient.

⑤过滤槽糟层厚度⑤ Thickness of filter tank bad layer

在糖化过程中，糖化醪所含有的不溶性麦糟将形成糟层。糟层厚度过厚，麦汁过滤速度缓慢，过滤时间延长；糟层厚度过薄，虽然提高过滤速度，但会降低麦汁透明度。生产中糟层厚度一般控制在30～50cm。且糟层厚度可以通过下式估算：During the mashing process, the insoluble spent grains contained in the mash will form a layer of spent grains. If the thickness of the bad layer is too thick, the filtration speed of the wort will be slow and the filtration time will be prolonged; if the thickness of the bad layer is too thin, although the filtration speed will be increased, the transparency of the wort will be reduced. The thickness of the dross layer in production is generally controlled at 30-50cm. And the thickness of the bad layer can be estimated by the following formula:

Figure GSA00000134752600033
式中，D为过滤槽设备的直径，
Figure GSA00000134752600034
为组分i的产槽率。
Figure GSA00000134752600033
In the formula, D is the diameter of the filter tank equipment,
Figure GSA00000134752600034
is the production rate of component i.

步骤3、利用变尺度蚁群优化方法对啤酒配方模型进行优化，最终求解最低成本的生产配方。具体步骤如下：Step 3. Using the variable-scale ant colony optimization method to optimize the beer formula model, and finally solve the production formula with the lowest cost. Specific steps are as follows:

①进行参数初始化，将每种组分所占质量百分比进行N等分，每种组分的离散间隔

Figure GSA00000134752600035
其中，
Figure GSA00000134752600036
为组分质量百分比的上下限。① Perform parameter initialization, divide the mass percentage of each component into N equal parts, and the discrete interval of each component
Figure GSA00000134752600035
in,
Figure GSA00000134752600036
is the upper and lower limit of the mass percentage of the component.

②若max(h1，h2，...，hn)≤ε，算法停止，输出当前最优方案，ε表示结束条件；否则转第3步；②If max(h 1 , h 2 ,...,h n )≤ε, the algorithm stops and outputs the current optimal solution, ε indicates the end condition; otherwise, go to step 3;

③(r，i)为第r组分上的第i个节点，其数值记为Xr，i；(r+1，j)为第r+1组分上的第j个节点；[(r，i)，(r+1)，j]为节点(r，i)到节点(r+1，j)的连线，蚁群中蚂蚁的数量为m在运动过程中，在时刻t蚂蚁k由位置(r，i)转移到位置(r+1，j)的概率为③(r, i) is the i-th node on the r-th component, and its value is recorded as X r, i ; (r+1, j) is the j-th node on the r+1-th component; [( r, i), (r+1), j] is the connection from node (r, i) to node (r+1, j), and the number of ants in the ant colony is m. During the movement, at time t the ants The probability of k moving from position (r, i) to position (r+1, j) is

pp [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] kk (( tt )) == ττ [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] (( tt )) ΣΣ jj == 11 Mm rr ++ 11 ττ [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] (( tt ))

式中：Mr+1是第r+1组分的允许取值范围，保证各品种比例之和不大于100％。如果第r+1品种不是最后一个待定品种，那么Mr+1取值范围为

Figure GSA00000134752600038
但对于最后一个品种，则Mr+1只能取值为
Figure GSA00000134752600039
τ[(r，i)，(r+1，j)](t)为时刻t在[(r，i)，(r+1)，j]连线上残留得信息素浓度强度，初始条件下各条路径上信息素浓度强度相等，即τ[(r，i)，(r+1，j)](0)＝C常数。In the formula: M r+1 is the allowable value range of the r+1th component, ensuring that the sum of the proportions of each variety is not greater than 100%. If the r+1th variety is not the last pending variety, then the value range of M r+1 is
Figure GSA00000134752600038
But for the last variety, M r+1 can only take the value
Figure GSA00000134752600039
τ [(r, i), (r+1, j)] (t) is the pheromone concentration remaining on the line [(r, i), (r+1), j] at time t, the initial condition The intensity of pheromone concentration on each of the following paths is equal, that is, τ [(r, i), (r+1, j)] (0) = C constant.

蚂蚁k(k＝1，2...m)是根据各条路径上概率进行随机选择。在创建解的过程中，蚂蚁访问到节点后，并且对其所走路径上的信息素采用局部信息素更新规则进行更新Ant k (k=1, 2...m) is randomly selected according to the probability on each path. In the process of creating a solution, after an ant visits a node, the pheromone on its path is updated using the local pheromone update rule

ττ [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] newnew == (( 11 -- ρρ )) ττ [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] oldold ++ ρρ ·&Center Dot; ΔτΔτ

式中，ρ是信息量衰减参数，(0＜ρ＜1)，令

Figure GSA00000134752600042
Q为常数，Jnn表示该蚂蚁得到方案的配方成本。每只蚂蚁如此递推并最终生成各自的配方方案。In the formula, ρ is the information decay parameter, (0<ρ<1), so that
Figure GSA00000134752600042
Q is a constant, and J nn represents the formulation cost of the scheme obtained by the ant. Each ant recurses in this way and finally generates its own formula.

④利用指标模型剔除不满足生产指标的不可行方案，对可行方案按下式计算配方成本J并将其与最好方案对比，如果配方成本J小于最好方案中的配方成本，则把该方案记为最好方案；④Use the index model to eliminate the infeasible schemes that do not meet the production indicators, and calculate the formula cost J of the feasible scheme according to the formula and compare it with the best scheme. If the formula cost J is less than the formula cost in the best scheme, then the scheme record as the best solution;

Figure GSA00000134752600043
其中Mi为原料组分i的单价。
Figure GSA00000134752600043
Where M i is the unit price of raw material component i.

⑤当所有蚂蚁均构建了配方方案，则按下式对信息素进行全局更新，得到信息素浓度强度

Figure GSA00000134752600044
⑤When all the ants have constructed the formula plan, the pheromone will be globally updated according to the following formula to obtain the pheromone concentration intensity
Figure GSA00000134752600044

Figure GSA00000134752600045
Figure GSA00000134752600045

式中：Jmin gb为开始迭代时所获得的最低目标函数值。In the formula: J min gb is the lowest objective function value obtained when the iteration starts.

⑥更新迭代次数t←t+1；若t≥tmax，则转到第3步；否则，找出当前最好方案，更新xr0、wr，其中wr＝2·hr，并按下式修正搜索上下限xrupper、xrlower，转到第1步，其中xr0为最好方案中第r组分的最佳质量百分比。⑥Update the number of iterations t←t+1; if t≥t max , go to step 3; otherwise, find out the current best solution, update x r0 , w r , where w r =2·h r , and press Modify the search upper and lower limits x rupper , x rlower by the following formula, go to step 1, where x r0 is the best mass percentage of the rth component in the best scheme.

xx rr upperupper &LeftArrow;&LeftArrow; 11 ,, xx rr lowerlower &LeftArrow;&LeftArrow; 11 &LeftArrow;&LeftArrow; ww rr ifif xx rr 00 ++ ww rr 22 >> 11 xx rr upperupper &LeftArrow;&LeftArrow; xx rr 00 ++ ww rr 22 ,, xx rr lowerlower &LeftArrow;&LeftArrow; xx rr 00 -- ww rr 22 ifif xx rr 00 ++ ww rr 22 &le;&le; 11 andand xx rr 00 -- ww rr 22 >> 00 xx rr upperupper &LeftArrow;&LeftArrow; ww rr ,, xx rr lowerlower &LeftArrow;&LeftArrow; 00 ifif xx rr 00 -- ww rr 22 &le;&le; 00

本发明相比于现有技术具有以下有益效果：本发明方法具有开放性、鲁棒性、并行性、全局收敛性以及对问题的数学形式无特殊要求等特点。Compared with the prior art, the present invention has the following beneficial effects: the method of the present invention has the characteristics of openness, robustness, parallelism, global convergence, and no special requirement on the mathematical form of the problem.

附图说明Description of drawings

图1为本发明方法中组分的离散间隔示意图。Figure 1 is a schematic diagram of the discrete compartments of the components in the process of the present invention.

具体实施方式Detailed ways

以下将结合附图对本发明作进一步描述。The present invention will be further described below in conjunction with the accompanying drawings.

本发明方法的包括以下步骤：The inventive method comprises the following steps:

步骤1、获取啤酒配方中主要原料(包括大麦麦芽、特种麦芽、小麦麦芽和辅助原料等)的产槽率、α氨基氮、糖化力、总可溶性氮、β葡聚糖以及单价等参数，这些数据可以通过供应商获得，也可通过生产过程中统计获取；Step 1. Obtain parameters such as tank production rate, α-amino nitrogen, saccharification power, total soluble nitrogen, β-glucan, and unit price of the main raw materials (including barley malt, special malt, wheat malt, and auxiliary raw materials) in the beer formula. The data can be obtained through the supplier, or through statistics during the production process;

步骤2、通过原料的参数建立综合生产性能指标估算模型，考虑的主要综合性能指标为麦芽糖化力、麦汁总氮、麦汁α氨基氮、麦汁β葡聚糖以及过滤槽糟层厚度。Step 2. Establish a comprehensive production performance index estimation model based on raw material parameters. The main comprehensive performance indexes considered are maltosaccharification power, wort total nitrogen, wort α-amino nitrogen, wort β-glucan, and lauter tank dross layer thickness.

①麦芽糖化力① Maltosaccharification power

在正常糖化操作下(65～68℃糖化30～45min)，每千克混合原料投料中，应含有1500～2000WK的糖化力。上限值可以缩短精化时间，并得到较高发酵度的麦汁；下限值糖化时间长，发酵度低。如小于1500WK会影响糖化作业、影响原料利用率、影响麦汁组成。且麦芽的总可溶性氮可按下式进行估算：Under normal saccharification operation (saccharification at 65-68°C for 30-45 minutes), the saccharification power of 1500-2000WK should be contained in every kilogram of mixed raw materials fed. The upper limit can shorten the refining time and obtain wort with a higher degree of fermentation; the lower limit can increase the saccharification time and lower the degree of fermentation. If it is less than 1500WK, it will affect the saccharification operation, affect the utilization rate of raw materials, and affect the composition of wort. And the total soluble nitrogen of malt can be estimated as follows:

式中，Ti、Xi分别为组分i的糖化力强度及质量分数，1≤i≤n； In the formula, T i and X i are the saccharification force intensity and mass fraction of component i respectively, 1≤i≤n;

②麦汁总氮②Wort total nitrogen

通常啤酒定型麦汁的总可溶性氮水平为：Usually the total soluble nitrogen level of beer finalized wort is:

全麦芽麦汁              900～1200mg/LWhole malt wort 900～1200mg/L

加辅料浓醇型啤酒麦汁    700～850mg/LConcentrated beer wort with auxiliary materials 700～850mg/L

加辅料淡爽型啤酒麦汁    550～700mg/LLight and refreshing beer wort with auxiliary materials 550～700mg/L

并且，麦汁的总可溶性氮可按下式进行估算：And, the total soluble nitrogen of wort can be estimated as follows:

Figure GSA00000134752600052
式中，
Figure GSA00000134752600053
为组分i的总氮质量浓度；A、V分别为一批次需要的原料总质量和要求的麦汁产量，单位分别为千克以及升；γ、
Figure GSA00000134752600054
分别为蛋白质分解强度和煮沸氮析出率。
Figure GSA00000134752600052
In the formula,
Figure GSA00000134752600053
is the total nitrogen mass concentration of component i; A and V are the total mass of raw materials required for a batch and the required wort output, respectively, in kilograms and liters; γ,
Figure GSA00000134752600054
Respectively, protein decomposition intensity and boiling nitrogen precipitation rate.

③麦汁α氨基氮③Wort α amino nitrogen

麦汁α氨基氮均与啤酒的风味物质高级醇和双乙酰相关，控制麦汁α氨基氮浓度对于控制啤酒风味尤为重要。一般麦汁α氨基氮控制在160～180mg/L范围内比较适宜。且麦汁α氨基氮含量可以通过下式估算：The α-amino nitrogen in wort is related to higher alcohols and diacetyl in beer, and controlling the concentration of α-amino nitrogen in wort is particularly important for controlling beer flavor. Generally, it is more appropriate to control wort α-amino nitrogen in the range of 160-180 mg/L. And the wort α-amino nitrogen content can be estimated by the following formula:

Figure GSA00000134752600055
式中，
Figure GSA00000134752600056
为组分i的α氨基氮质量浓度，κ为氨基氮系数。
Figure GSA00000134752600055
In the formula,
Figure GSA00000134752600056
is the α-amino nitrogen mass concentration of component i, and κ is the amino-nitrogen coefficient.

④麦汁β葡聚糖④Wort β-glucan

啤酒中含有适量的β葡聚糖是保持啤酒具有醇厚感的物质之一，但是如果过多的β葡聚糖会在制麦中分解不足，在啤酒酿造过程中会带来一系列相关的问题。因此在制麦过程β葡聚糖的含量低于250mg/L。且麦汁β葡聚糖含量可以通过下式估算：An appropriate amount of β-glucan in beer is one of the substances that maintain the mellow taste of beer, but if too much β-glucan will not be decomposed enough during malting, it will cause a series of related problems in the beer brewing process . Therefore, the content of β-glucan in the malting process is lower than 250mg/L. And the wort β-glucan content can be estimated by the following formula:

式中，为组分i的β葡聚糖质量浓度，ν为葡聚糖系数。 In the formula, is the mass concentration of β-glucan in component i, and ν is the glucan coefficient.

⑤过滤槽糟层厚度⑤ Thickness of filter tank bad layer

在糖化过程中，糖化醪所含有的不溶性麦糟将形成糟层。糟层厚度过厚，麦汁过滤速度缓慢，过滤时间延长；糟层厚度过薄，虽然提高过滤速度，但会降低麦汁透明度。生产中糟层厚度一般控制在30～50cm。且糟层厚度可以通过下式估算：During the mashing process, the insoluble spent grains contained in the mash will form a layer of spent grains. If the thickness of the bad layer is too thick, the filtration speed of the wort will be slow and the filtration time will be prolonged; if the thickness of the bad layer is too thin, although the filtration speed will be increased, the transparency of the wort will be reduced. The thickness of the dross layer in production is generally controlled at 30-50cm. And the thickness of the bad layer can be estimated by the following formula:

Figure GSA00000134752600063
式中，D为过滤槽设备的直径，
Figure GSA00000134752600064
为组分i的产槽率。
Figure GSA00000134752600063
In the formula, D is the diameter of the filter tank equipment,
Figure GSA00000134752600064
is the production rate of component i.

步骤3、利用变尺度蚁群优化方法对啤酒配方模型进行优化，最终求解最低成本的生产配方。具体步骤如下：Step 3. Using the variable-scale ant colony optimization method to optimize the beer formula model, and finally solve the production formula with the lowest cost. Specific steps are as follows:

①进行参数初始化，将每种组分所占质量百分比进行N等分，每种组分的离散间隔

Figure GSA00000134752600065
其中，为组分质量百分比的上下限。① Perform parameter initialization, divide the mass percentage of each component into N equal parts, and the discrete interval of each component
Figure GSA00000134752600065
in, is the upper and lower limit of the mass percentage of the component.

如图1所示，图中每根垂直条分线代表一种原料，并被离散成N等分，离散的节点代表该成分的质量分数。经过离散处理以后，配方优化问题转变为寻找最优路径的问题。蚂蚁从start点出发，逐级经过每个品种上的节点，最后到达end点，完成一次循环并形成一个完整的配方。As shown in Figure 1, each vertical bar in the figure represents a raw material and is discretized into N equal parts, and the discrete nodes represent the mass fraction of the component. After discrete processing, the formula optimization problem is transformed into the problem of finding the optimal path. Ants start from the start point, go through the nodes of each species step by step, and finally reach the end point, completing a cycle and forming a complete recipe.

②若max(h1，h2，...，hn)≤ε，算法停止，输出当前最优方案，ε表示结束条件；否则转第3步；②If max(h 1 , h 2 ,...,h n )≤ε, the algorithm stops and outputs the current optimal solution, ε indicates the end condition; otherwise, go to step 3;

③(r，i)为第r组分上的第i个节点，其数值记为Xr，i；(r+1，j)为第r+1组分上的第j个节点；[(r，i)，(r+1)，j]为节点(r，i)到节点(r+1，j)的连线，蚁群中蚂蚁的数量为m在运动过程中，在时刻t蚂蚁k由位置(r，i)转移到位置(r+1，j)的概率为③(r, i) is the i-th node on the r-th component, and its value is recorded as X r, i ; (r+1, j) is the j-th node on the r+1-th component; [( r, i), (r+1), j] is the connection from node (r, i) to node (r+1, j), and the number of ants in the ant colony is m. During the movement, at time t the ants The probability of k moving from position (r, i) to position (r+1, j) is

pp [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] kk (( tt )) == &tau;&tau; [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] (( tt )) &Sigma;&Sigma; jj == 11 Mm rr ++ 11 &tau;&tau; [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] (( tt ))

式中：Mr+1是第r+1组分的允许取值范围，保证各品种比例之和不大于100％。如果第r+1品种不是最后一个待定品种，那么Mr+1取值范围为

Figure GSA00000134752600072
但对于最后一个品种，则Mr+1只能取值为
Figure GSA00000134752600073
为时刻t在[(r，i)，(r+1)，j]连线上残留得信息素浓度强度，初始条件下各条路径上信息素浓度强度相等，即τ[(r，i)，(r+1，j)](0)＝C常数。In the formula: M r+1 is the allowable value range of the r+1th component, ensuring that the sum of the proportions of each variety is not greater than 100%. If the r+1th variety is not the last pending variety, then the value range of M r+1 is
Figure GSA00000134752600072
But for the last variety, M r+1 can only take the value
Figure GSA00000134752600073
is the remaining pheromone concentration intensity on the line [(r, i), (r+1), j] at time t, and the pheromone concentration intensity on each path is equal under the initial condition, that is, τ [(r, i) , (r+1, j)] (0)=C constant.

蚂蚁k(k＝1，2...m)是根据各条路径上概率进行随机选择。在创建解的过程中，蚂蚁访问到节点后，并且对其所走路径上的信息素采用局部信息素更新规则进行更新Ant k (k=1, 2...m) is randomly selected according to the probability on each path. In the process of creating a solution, after an ant visits a node, the pheromone on its path is updated using the local pheromone update rule

&tau;&tau; [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] newnew == (( 11 -- &rho;&rho; )) &tau;&tau; [[ (( rr ,, ii )) ,, (( rr ++ 11 ,, jj )) ]] oldold ++ &rho;&rho; &CenterDot;&Center Dot; &Delta;&tau;&Delta;&tau;

式中，ρ是信息量衰减参数，(0＜ρ＜1)，令

Figure GSA00000134752600075
Q为常数，Jnn表示该蚂蚁得到方案配方成本。每只蚂蚁如此递推并最终生成各自的配方方案。In the formula, ρ is the information decay parameter, (0<ρ<1), so that
Figure GSA00000134752600075
Q is a constant, and J nn means that the ant gets the formula cost of the scheme. Each ant recurses in this way and finally generates its own formula.

④利用指标模型剔除不满足生产指标的不可行方案，对可行方案按下式计算配方成本J并将其与最好方案对比，如果配方成本J小于最好方案中的配方成本，则把该方案记为最好方案；④Use the index model to eliminate infeasible schemes that do not meet the production indicators, and calculate the formula cost J of the feasible scheme according to the formula and compare it with the best scheme. If the formula cost J is less than the formula cost in the best scheme, then the scheme record as the best solution;

Figure GSA00000134752600076
其中Mi为原料组分i的单价。
Figure GSA00000134752600076
Where M i is the unit price of raw material component i.

⑤当所有蚂蚁均构建了配方方案，则按下式对信息素进行全局更新，得到信息素浓度强度 ⑤When all the ants have constructed the formula plan, the pheromone will be globally updated according to the following formula to obtain the pheromone concentration intensity

式中：Jmin gb为开始迭代时所获得的最低目标函数值。In the formula: J min gb is the lowest objective function value obtained when the iteration starts.

⑥更新迭代次数t←t+1；若t≥tmax，则转到第3步；否则，找出当前最好方案，更新xr0、wr，其中wr＝2·hr，并按下式修正搜索上下限xrupper、xrlower，转到第1步，其中xr0为最好方案中第r组分的最佳质量百分比。⑥Update the number of iterations t←t+1; if t≥t max , go to step 3; otherwise, find out the current best solution, update x r0 , w r , where w r =2·h r , and press Modify the search upper and lower limits x rupper , x rlower by the following formula, go to step 1, where x r0 is the best mass percentage of the rth component in the best scheme.

xx rr upperupper &LeftArrow;&LeftArrow; 11 ,, xx rr lowerlower &LeftArrow;&LeftArrow; 11 &LeftArrow;&LeftArrow; ww rr ifif xx rr 00 ++ ww rr 22 >> 11 xx rr upperupper &LeftArrow;&LeftArrow; xx rr 00 ++ ww rr 22 ,, xx rr lowerlower &LeftArrow;&LeftArrow; xx rr 00 -- ww rr 22 ifif xx rr 00 ++ ww rr 22 &le;&le; 11 andand xx rr 00 -- ww rr 22 >> 00 xx rr upperupper &LeftArrow;&LeftArrow; ww rr ,, xx rr lowerlower &LeftArrow;&LeftArrow; 00 ifif xx rr 00 -- ww rr 22 &le;&le; 00

Claims (1)

1. A beer production formula optimization method is characterized by comprising the following steps:
step 1, obtaining the yield, alpha amino nitrogen, saccharifying power, total soluble nitrogen, beta glucan and unit price of main raw materials in a beer formula;
step 2, establishing a malt saccharification force, wort total nitrogen, wort alpha-amino nitrogen, wort beta-glucan and filter tank grain layer thickness comprehensive production performance index estimation model;
Figure FSA00000134752500011
in the formula, Ti、xiRespectively representing the saccharifying power intensity and mass fraction of the component i, wherein i is more than or equal to 1 and less than or equal to n;
Figure FSA00000134752500012
in the formula,
Figure FSA00000134752500013
is the total nitrogen mass concentration of component i; A. v is the total mass of raw materials required by a batch and the required wort yield, and the unit is kilogram and liter respectively; gamma, gamma,The protein decomposition strength and the boiling nitrogen precipitation rate are respectively measured;
Figure FSA00000134752500015
in the formula,
Figure FSA00000134752500016
is the mass concentration of alpha amino nitrogen of the component i, and kappa is the amino nitrogen coefficient;
Figure FSA00000134752500017
in the formula,
Figure FSA00000134752500018
is the beta glucan mass concentration of component i, and v is the glucan coefficient;
Figure FSA00000134752500019
wherein D is the diameter of the filter tank equipment,
Figure FSA000001347525000110
the yield of component i is the yield of component i;
and 3, optimizing the beer formula model by using a variable-scale ant colony optimization method, and finally solving the production formula with the lowest cost, wherein the specific steps are as follows:
firstly, initializing parameters, dividing each component into N equal parts by mass percentage, and performing discrete interval of each componentWherein,
Figure FSA000001347525000112
respectively representing the upper limit and the lower limit of the mass percentage of the components;
if max (h)1，h2，...，hn) Stopping the algorithm when the epsilon is less than or equal to epsilon, and outputting the current optimal scheme, wherein epsilon represents the ending condition; otherwise, turning to the step 3;
(r, i) is the ith node on the r component, and the numerical value is marked as Xr，i(ii) a (r +1, j) is the jth node on the r +1 component; [ (r, i), (r +1), j)]Is a connecting line from the node (r, i) to the node (r +1, j), the number of ants in the ant group is m, and the probability that the ant k is transferred from the position (r, i) to the position (r +1, j) at the moment t in the motion process is
<math><mrow><msubsup><mi>p</mi><mrow><mo>[</mo><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>i</mi><mo>)</mo></mrow><mo>,</mo><mrow><mo>(</mo><mi>r</mi><mo>+</mo><mn>1</mn><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>]</mo></mrow><mi>k</mi></msubsup><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><mfrac><mrow><msub><mi>&tau;</mi><mrow><mo>[</mo><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>i</mi><mo>)</mo></mrow><mo>,</mo><mrow><mo>(</mo><mi>r</mi><mo>+</mo><mn>1</mn><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>]</mo></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow><mrow><munderover><mi>&Sigma;</mi><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><msub><mi>M</mi><mrow><mi>r</mi><mo>+</mo><mn>1</mn></mrow></msub></munderover><msub><mi>&tau;</mi><mrow><mo>[</mo><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>i</mi><mo>)</mo></mrow><mo>,</mo><mrow><mo>(</mo><mi>r</mi><mo>+</mo><mn>1</mn><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>]</mo></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></mfrac></mrow></math>
In the formula: mr+1Is the allowable value range of the r +1 th component, tau[(r，i)，(r+1，j)](t) at time t at [ (r, i), (r +1), j]Residual pheromone concentration intensity on the connecting line is obtained;
an ant k (k is 1, 2.. m) is randomly selected according to the probability on each path, and after the ant visits a node, the pheromone on the path where the ant walks is updated by adopting a local pheromone updating rule
<math><mrow><msubsup><mi>&tau;</mi><mrow><mo>[</mo><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>i</mi><mo>)</mo></mrow><mo>,</mo><mrow><mo>(</mo><mi>r</mi><mo>+</mo><mn>1</mn><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>]</mo></mrow><mi>new</mi></msubsup><mo>=</mo><mrow><mo>(</mo><mn>1</mn><mo>-</mo><mi>&rho;</mi><mo>)</mo></mrow><msubsup><mi>&tau;</mi><mrow><mo>[</mo><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>i</mi><mo>)</mo></mrow><mo>,</mo><mrow><mo>(</mo><mi>r</mi><mo>+</mo><mn>1</mn><mo>,</mo><mi>j</mi><mo>)</mo></mrow><mo>]</mo></mrow><mi>old</mi></msubsup><mo>+</mo><mi>&rho;</mi><mo>&CenterDot;</mo><mi>&Delta;&tau;</mi></mrow></math>
Where ρ is an information amount attenuation parameter, (0 < ρ < 1), let
Figure FSA00000134752500023
Q is a constant, JnnRepresenting the formula cost of the ant obtaining scheme, and each ant recurs in such a way and finally generates a respective formula scheme;
fourthly, eliminating the infeasible schemes which do not meet the production index by using the index modelCalculating a formula cost J by the scheme and comparing the formula cost J with the best scheme, and if the formula cost J is less than the formula cost in the best scheme, recording the scheme as the best scheme;wherein M isiIs the unit price of the raw material component i;
fifthly, when all ants construct a formula scheme, globally updating the pheromone according to the following formula to obtain the pheromone concentration intensity tau'[(r，i)，(r+1，j)]
Figure FSA00000134752500025
In the formula: j. the design is a squaremingbThe lowest objective function value obtained when the iteration is started;
sixthly, updating iteration times t ← t + 1; if t is more than or equal to tmaxTurning to the step 3; otherwise, find the best scheme at present, update xr0、wrWherein w isr＝2·hrAnd correcting the upper and lower search limits x according to the following formularupper、xrlowerGo to step 1, where xr0For the best mass percentage of the r-th component in the best case,
<math><mrow><mfenced open='{' close=''><mtable><mtr><mtd><msubsup><mi>x</mi><mi>r</mi><mi>upper</mi></msubsup><mo>&LeftArrow;</mo><mn>1</mn><mo>,</mo><msubsup><mi>x</mi><mi>r</mi><mi>lower</mi></msubsup><mo>&LeftArrow;</mo><mn>1</mn><mo>-</mo><msub><mi>w</mi><mi>r</mi></msub></mtd><mtd><mi>if</mi></mtd><mtd><msub><mi>x</mi><mrow><mi>r</mi><mn>0</mn></mrow></msub><mo>+</mo><mfrac><msub><mi>w</mi><mi>r</mi></msub><mn>2</mn></mfrac><mo>></mo><mn>1</mn></mtd></mtr><mtr><mtd><msubsup><mi>x</mi><mi>r</mi><mi>upper</mi></msubsup><mo>&LeftArrow;</mo><msub><mi>x</mi><mrow><mi>r</mi><mn>0</mn></mrow></msub><mo>+</mo><mfrac><msub><mi>w</mi><mi>r</mi></msub><mn>2</mn></mfrac><mo>,</mo><msubsup><mi>x</mi><mi>r</mi><mi>lower</mi></msubsup><mo>&LeftArrow;</mo><msub><mi>x</mi><mrow><mi>r</mi><mn>0</mn></mrow></msub><mo>-</mo><mfrac><msub><mi>w</mi><mi>r</mi></msub><mn>2</mn></mfrac></mtd><mtd><mi>if</mi></mtd><mtd><msub><mi>x</mi><mrow><mi>r</mi><mn>0</mn></mrow></msub><mo>+</mo><mfrac><msub><mi>w</mi><mi>r</mi></msub><mn>2</mn></mfrac><mo>&le;</mo><mn>1</mn><mi>and</mi><msub><mi>x</mi><mrow><mi>r</mi><mn>0</mn></mrow></msub><mo>-</mo><mfrac><msub><mi>w</mi><mi>r</mi></msub><mn>2</mn></mfrac><mo>></mo><mn>0</mn></mtd></mtr><mtr><mtd><msubsup><mi>x</mi><mi>r</mi><mi>upper</mi></msubsup><mo>&LeftArrow;</mo><msub><mi>w</mi><mi>r</mi></msub><mo>,</mo><msubsup><mi>x</mi><mi>r</mi><mi>lower</mi></msubsup><mo>&LeftArrow;</mo><mn>0</mn></mtd><mtd><mi>if</mi></mtd><mtd><msub><mi>x</mi><mrow><mi>r</mi><mn>0</mn></mrow></msub><mo>-</mo><mfrac><msub><mi>w</mi><mi>r</mi></msub><mn>2</mn></mfrac><mo>&le;</mo><mn>0</mn></mtd></mtr></mtable></mfenced><mo>.</mo></mrow></math>
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103548048A (en) * 2010-11-05 2014-01-29 可口可乐公司 A method of juice production, apparatus and system
CN104035327A (en) * 2014-05-30 2014-09-10 杭州电子科技大学 Production scheduling optimization method for beer saccharification process
CN110109430A (en) * 2019-04-30 2019-08-09 杭州电子科技大学 A kind of intermittent beer fermenting device Optimal Control System
CN117281752A (en) * 2023-08-25 2023-12-26 广东泛华生物科技有限公司 Composition with scalp and hair cleaning and moisturizing effects, and preparation method and application thereof

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1431292A (en) * 2002-12-30 2003-07-23 上海交通大学 Method for optimizing deployment of biofermentation batch procedure
US20070282583A1 (en) * 2006-05-31 2007-12-06 Kapadi Mangesh D System and method for sugarcane recovery estimation
CN101430776A (en) * 2007-11-05 2009-05-13 云南农业大学 Fuzzy integrated judging method for feed formula

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1431292A (en) * 2002-12-30 2003-07-23 上海交通大学 Method for optimizing deployment of biofermentation batch procedure
US20070282583A1 (en) * 2006-05-31 2007-12-06 Kapadi Mangesh D System and method for sugarcane recovery estimation
CN101430776A (en) * 2007-11-05 2009-05-13 云南农业大学 Fuzzy integrated judging method for feed formula

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
《计算机工程与应用》 20111130 郑松等 啤酒原料配方的蚁群优化设计研究 226-228，244 1 第47卷, 第11期 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103548048A (en) * 2010-11-05 2014-01-29 可口可乐公司 A method of juice production, apparatus and system
CN104035327A (en) * 2014-05-30 2014-09-10 杭州电子科技大学 Production scheduling optimization method for beer saccharification process
CN110109430A (en) * 2019-04-30 2019-08-09 杭州电子科技大学 A kind of intermittent beer fermenting device Optimal Control System
CN117281752A (en) * 2023-08-25 2023-12-26 广东泛华生物科技有限公司 Composition with scalp and hair cleaning and moisturizing effects, and preparation method and application thereof

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