## 2 The Literature Review
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Production, Water Intake Directions**}} & \multicolumn{2}{c}{**Unit water Consumption Indices**} & \multirow{2}{*}{**Source**} \\ \cline{2-3} \cline{5-5} \multicolumn{1}{c}{**Index Range \({}^{\star}\)**} & & & \\ \hline Nectars & & 16.0 & \\ Liquid fruit & & 9.0–11.0 & \\ Tomato juice & & 13.0 & \\ Concentrated fruit juices & & 140.0 & \\ \hline Canned fruit & & 2.5–4 & \\ Canned vegetables & & 3.5–6 & \\ Frozen vegetables & & 5.0–8.5 & \\ Fruit juices & & 6.5 & \\ Jams & & 6.0 & \\ Preserves for children & & 6.0–9.0 & \\ \hline Frozen fruit & \(Z\) & 1.3–4.0 & [13] \\ \hline Fruit processing & & \(Z\) & 2.2–8.2 & \\ Vegetable processing & & 5.8–20.3 & [14] \\ \hline \multirow{3}{*}{Fruit and vegetable processing plants} & \multirow{3}{*}{Poland} & \multirow{3}{*}{\(Z\)} & 12.0–32.0 ** & [15] \\ \cline{3-3} \cline{5-5} \cline{5-  
The following formula was adopted to explain the dependence of y on independent variables (being the actual parameters observed in practice or their functions):  
\[y=b_{0}+b_{1}\;x_{1}+b_{2}\;x_{2}+.......+b_{k}\;x_{k}; \tag{1}\]  
where \(y\) is the dependent variable (\(A_{w}\) or \(W_{w}\)) and \(x\) represents the independent variables (presented in Table 3). Factor groups I-III contributed to the impact on daily water consumption \(A_{w}\), ranging from 39% to 54%. Admittedly, daily water consumption \(A_{w}\) showed high disproportions among the analyzed plants, but its variability was 54%, attributable to the power of the electrical devices installed in the plant boiler room, pump room, and water treatment station (\(P_{1}\)). The production structure expressed as \(Z_{1}\) (production of frozen vegetables), \(Z_{2}\) (production of fruit and vegetable concentrates), and \(Z_{3}\) (production of drinks) covered by group II contributed about 45% of the impact on the daily water consumption \(A_{w}\). The application of factor group IV is a source of information on the total impact of technical and technological factors, the degree of automation of the production operations, and organizational-production factors on water consumption. The variability of the unit water consumption \(W_{w}\) was 84.3%, attributable to the impact of factor \(K_{2}\) (total cubic volume per 1 mg of raw material processed in a day). The application of the obtained equations depends on the ranges of the specific independent variables, the numerical values of which are presented in Table 3.  
The reasons for excess water consumption usually include:  
* Lack of fully closed circuits of process water (e.g., from washing) and (mainly) cooling water [23];
* Ineffective recovery of the condensate;
* Lack of water consumption optimization in the washing processes (both automatic and manual); the leakage of pipelines, valves, and machinery; and the lack of full supervision over water consumption in specific technological processes. In the ongoing operation of the production plants, the factor groups I, II, and IV specified in Table 1 have a minor impact on water consumption rationalization. The main impact on rationalization of water consumption is exerted by factor group III. Reduction of unit water consumption must be achieved by the application of the best available techniques in the scope of water management [24]. Rationalization of water consumption in fruit and vegetable industry plants is possible, mostly for entities that carry out monitoring with this scope. The research results presented in Table 3 only partially explain the impact of the processing structure (factor group III) on water consumption. The regression coefficients